diff --git a/myems-bacnet/LICENSE b/myems-bacnet/LICENSE
index c6cedd28..b91c1ac4 100644
--- a/myems-bacnet/LICENSE
+++ b/myems-bacnet/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 MyEMS
+Copyright (c) 2021 MyEMS
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/myems-cleaning/LICENSE b/myems-cleaning/LICENSE
new file mode 100644
index 00000000..b91c1ac4
--- /dev/null
+++ b/myems-cleaning/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 MyEMS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/myems-cleaning/README.md b/myems-cleaning/README.md
new file mode 100644
index 00000000..729d0a6b
--- /dev/null
+++ b/myems-cleaning/README.md
@@ -0,0 +1,61 @@
+# myems-cleaning
+MyEMS Cleaning Service 
+
+MyEMS 数据清洗服务
+
+
+### Introduction
+
+This service is a component of MyEMS and it cleans the historical data. 
+
+[![Codacy Badge](https://api.codacy.com/project/badge/Grade/ac9730a315c4436cb224dff0eedd7eaf)](https://app.codacy.com/gh/myems/myems-cleaning?utm_source=github.com&utm_medium=referral&utm_content=myems/myems-cleaning&utm_campaign=Badge_Grade)
+[![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/myems/myems-cleaning/badges/quality-score.png?b=master)](https://scrutinizer-ci.com/g/myems/myems-cleaning/?branch=master)
+[![Maintainability](https://api.codeclimate.com/v1/badges/e81df9e97fb701e0865d/maintainability)](https://codeclimate.com/github/myems/myems-cleaning/maintainability)
+[![Total alerts](https://img.shields.io/lgtm/alerts/g/myems/myems-cleaning.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/myems/myems-cleaning/alerts/)
+
+
+### Prerequisites
+
+mysql.connector
+
+
+### Installation
+    
+Download and install MySQL Connector:
+```
+    $ cd ~/tools
+    $ wget https://dev.mysql.com/get/Downloads/Connector-Python/mysql-connector-python-8.0.20.tar.gz
+    $ tar xzf mysql-connector-python-8.0.20.tar.gz
+    $ cd ~/tools/mysql-connector-python-8.0.20
+    $ sudo python3 setup.py install
+```
+
+Install myems-cleaning service
+```bash
+    $ cd ~
+    $ git clone https://github.com/myems.git
+    $ sudo git checkout master (or the latest release tag)
+    $ sudo cp -R ~/myems/myems-cleaning /myems-cleaning
+    $ cd /myems-cleaning
+```
+Open config file and edit database configuration
+```bash
+    $ sudo nano config.py
+```
+Setup systemd service:
+```bash
+    $ sudo cp myems-cleaning.service /lib/systemd/system/
+```
+Enable the service:
+```bash
+    $ sudo systemctl enable myems-cleaning.service
+```
+Start the service:
+```bash
+    $ sudo systemctl start myems-cleaning.service
+```
+
+### References
+
+1. https://myems.io
+2. https://dev.mysql.com/doc/connector-python/en/
\ No newline at end of file
diff --git a/myems-cleaning/clean_analog_value.py b/myems-cleaning/clean_analog_value.py
new file mode 100644
index 00000000..2cba4254
--- /dev/null
+++ b/myems-cleaning/clean_analog_value.py
@@ -0,0 +1,50 @@
+import mysql.connector
+import config
+import time
+from datetime import datetime, timedelta
+import schedule
+
+
+def job(logger):
+
+    cnx = None
+    cursor = None
+    try:
+        cnx = mysql.connector.connect(**config.myems_historical_db)
+        cursor = cnx.cursor()
+    except Exception as e:
+        logger.error("Error in clean analog value process " + str(e))
+        if cursor:
+            cursor.close()
+        if cnx:
+            cnx.close()
+        return
+
+    expired_utc = datetime.utcnow() - timedelta(days=config.live_in_days)
+    try:
+        cursor.execute(" DELETE FROM tbl_analog_value WHERE utc_date_time < %s ", (expired_utc,))
+        cnx.commit()
+    except Exception as e:
+        logger.error("Error in delete_expired_trend process " + str(e))
+    finally:
+        if cursor:
+            cursor.close()
+        if cnx:
+            cnx.close()
+
+    logger.info("Deleted trend before date time in UTC: " + expired_utc.isoformat()[0:19])
+
+
+def process(logger):
+
+    if config.is_debug:
+        # run the job immediately
+        job(logger)
+        return
+
+    schedule.every(8).hours.do(job, logger)
+
+    while True:
+        schedule.run_pending()
+        time.sleep(60)
+
diff --git a/myems-cleaning/clean_digital_value.py b/myems-cleaning/clean_digital_value.py
new file mode 100644
index 00000000..a3ebf631
--- /dev/null
+++ b/myems-cleaning/clean_digital_value.py
@@ -0,0 +1,50 @@
+import mysql.connector
+import config
+import time
+from datetime import datetime, timedelta
+import schedule
+
+
+def job(logger):
+
+    cnx = None
+    cursor = None
+    try:
+        cnx = mysql.connector.connect(**config.myems_historical_db)
+        cursor = cnx.cursor()
+    except Exception as e:
+        logger.error("Error in clean digital value process " + str(e))
+        if cursor:
+            cursor.close()
+        if cnx:
+            cnx.close()
+        return
+
+    expired_utc = datetime.utcnow() - timedelta(days=config.live_in_days)
+    try:
+        cursor.execute(" DELETE FROM tbl_digital_value WHERE utc_date_time < %s ", (expired_utc,))
+        cnx.commit()
+    except Exception as e:
+        logger.error("Error in delete_expired_trend process " + str(e))
+    finally:
+        if cursor:
+            cursor.close()
+        if cnx:
+            cnx.close()
+
+    logger.info("Deleted trend before date time in UTC: " + expired_utc.isoformat()[0:19])
+
+
+def process(logger):
+
+    if config.is_debug:
+        # run the job immediately
+        job(logger)
+        return
+
+    schedule.every(8).hours.do(job, logger)
+
+    while True:
+        schedule.run_pending()
+        time.sleep(60)
+
diff --git a/myems-cleaning/clean_energy_value.py b/myems-cleaning/clean_energy_value.py
new file mode 100644
index 00000000..8a358da9
--- /dev/null
+++ b/myems-cleaning/clean_energy_value.py
@@ -0,0 +1,559 @@
+import mysql.connector
+import config
+import time
+from datetime import datetime
+
+
+########################################################################################################################
+# This procedure will find and tag the bad energy values.
+#
+# Step 1: get the time slot to clean.
+# Step 2: check bad case class 1 with high limits and low limits.
+# Step 3: check bad case class 2 which is in concave shape model.
+# Step 4: tag the is_bad property of energy values.
+########################################################################################################################
+
+def process(logger):
+
+    while True:
+        # the outermost loop to reconnect server if there is a connection error
+        cnx = None
+        cursor = None
+        try:
+            cnx = mysql.connector.connect(**config.myems_historical_db)
+            cursor = cnx.cursor()
+        except Exception as e:
+            logger.error("Error at the begin of clean_energy_value.process " + str(e))
+            if cursor:
+                cursor.close()
+            if cnx:
+                cnx.close()
+            time.sleep(60)
+            continue
+
+        # Note:
+        # the default value of unchecked values' is_bad property is NULL
+        # if a value is checked and the result is bad then is_bad would be set to TRUE
+        # else if a value is checked and the result is good then is_bad would be set to FALSE
+
+        ################################################################################################################
+        # Step 1: get the time slot to clean.
+        ################################################################################################################
+
+        min_datetime = None
+        max_datetime = None
+        try:
+            query = (" SELECT MIN(utc_date_time), MAX(utc_date_time) "
+                     " FROM tbl_energy_value "
+                     " WHERE is_bad IS NULL ")
+            cursor.execute(query, ())
+            row_datetime = cursor.fetchone()
+            if row_datetime is not None and len(row_datetime) == 2 and \
+                    isinstance(row_datetime[0], datetime) and isinstance(row_datetime[1], datetime):
+                min_datetime = row_datetime[0]
+                max_datetime = row_datetime[1]
+
+        except Exception as e:
+            logger.error("Error in Step 1 of clean_energy_value.process " + str(e))
+            if cursor:
+                cursor.close()
+            if cnx:
+                cnx.close()
+            time.sleep(60)
+            continue
+        print("min_datetime: " + min_datetime.isoformat()[0:19])
+        print("max_datetime: " + max_datetime.isoformat()[0:19])
+
+        ################################################################################################################
+        # Step 2: check bad case class 1 with high limits and low limits.
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 1.1
+        # id          point_id utc_date_time        actual_value          is_bad (expected)
+        # 104814811	  3333     2018-01-31 16:45:04	115603.0078125        good
+        # 104814588	  3333     2018-01-31 16:44:00	115603.0078125        good
+        # 104815007	  3333     2018-01-31 16:46:09	1.832278249396618e21  bad
+        # 104815226	  3333     2018-01-31 16:47:13	1.832278249396618e21  bad
+        # 104815423	  3333     2018-01-31 16:48:17	1.832278249396618e21  bad
+        # 104815643	  3333     2018-01-31 16:49:22	1.832278249396618e21  bad
+        # 104815820	  3333     2018-01-31 16:50:26	1.832278249396618e21  bad
+        # 104816012	  3333     2018-01-31 16:51:30	1.832278249396618e21  bad
+        # 104816252	  3333     2018-01-31 16:52:34	1.832278249396618e21  bad
+        # 104816446	  3333     2018-01-31 16:53:38	1.832278249396618e21  bad
+        # 104816667	  3333     2018-01-31 16:54:43	1.832278249396618e21  bad
+        # 104816860	  3333     2018-01-31 16:55:47	1.832278249396618e21  bad
+        # 104817065	  3333     2018-01-31 16:56:51	1.832278249396618e21  bad
+        # 104817284	  3333     2018-01-31 16:57:55	1.832278249396618e21  bad
+        # 104817482	  3333     2018-01-31 16:58:59	1.832278249396618e21  bad
+        # 104817723	  3333     2018-01-31 17:00:04	1.832278249396618e21  bad
+        # 104817940	  3333     2018-01-31 17:01:08	115749.0078125        good
+        # 104818142	  3333     2018-01-31 17:02:11	115749.0078125        good
+        # 104818380	  3333     2018-01-31 17:03:16	115749.0078125        good
+        # 104818596	  3333     2018-01-31 17:04:20	115749.0078125        good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 1.2:
+        # id    point_id  utc_date_time          actual_value           is_bad (expected)
+        #       3333      2018-01-31 17:27:53    115823.0078125         good
+        #       3333      2018-01-31 17:28:57    115823.0078125         good
+        #       3333      2018-01-31 17:30:02    115823.0078125         good
+        #       3333      2018-01-31 17:31:06    115823.0078125         good
+        #       3333      2018-01-31 17:32:11    0                      bad
+        #       3333      2018-01-31 17:33:15    0                      bad
+        #       3333      2018-01-31 17:34:19    0                      bad
+        #       3333      2018-01-31 17:35:24    0                      bad
+        #       3333      2018-01-31 17:36:28    0                      bad
+        #       3333      2018-01-31 17:37:32    0                      bad
+        #       3333      2018-01-31 17:38:36    0                      bad
+        #       3333      2018-01-31 17:39:41    0                      bad
+        #       3333      2018-01-31 17:40:44    0                      bad
+        #       3333      2018-01-31 17:41:49    0                      bad
+        #       3333      2018-01-31 17:43:57    0                      bad
+        #       3333      2018-01-31 17:42:53    0                      bad
+        #       3333      2018-01-31 17:45:01    0                      bad
+        #       3333      2018-01-31 17:46:06    0                      bad
+        #       3333      2018-01-31 17:47:10    0                      bad
+        #       3333      2018-01-31 17:48:14    115969.0078125         good
+        #       3333      2018-01-31 17:49:18    115969.0078125         good
+        #       3333      2018-01-31 17:50:22    115969.0078125         good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 1.3:
+        # id    point_id  utc_date_time          actual_value           is_bad (expected)
+        #       3333      2018-02-04 07:00:38    139968                  good
+        #       3333      2018-02-04 07:01:42    139968                  good
+        #       3333      2018-02-04 07:03:54    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:04:58    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:06:03    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:07:06    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:08:10    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:09:13    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:10:17    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:11:21    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:12:25    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:13:29    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:14:33    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:15:37    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:16:41    -7.068193740872921e-3   bad
+        #       3333      2018-02-04 07:17:45    140114                  good
+        #       3333      2018-02-04 07:18:49    140114                  good
+        #       3333      2018-02-04 07:19:53    140114                  good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 1.4:
+        # id    point_id  utc_date_time          actual_value           is_bad (expected)
+        #       3333      2018-02-08 01:16:38    165746.015625          good
+        #       3333      2018-02-08 01:15:34    165746.015625          good
+        #       3333      2018-02-08 01:14:30    165746.015625          good
+        #       3333      2018-02-08 01:13:27    0.00303281145170331    bad
+        #       3333      2018-02-08 01:12:22    0.00303281145170331    bad
+        #       3333      2018-02-08 01:11:19    0.00303281145170331    bad
+        #       3333      2018-02-08 01:10:15    0.00303281145170331    bad
+        #       3333      2018-02-08 01:09:11    0.00303281145170331    bad
+        #       3333      2018-02-08 01:08:06    0.00303281145170331    bad
+        #       3333      2018-02-08 01:07:02    0.00303281145170331    bad
+        #       3333      2018-02-08 01:05:58    0.00303281145170331    bad
+        #       3333      2018-02-08 01:04:54    0.00303281145170331    bad
+        #       3333      2018-02-08 01:03:50    0.00303281145170331    bad
+        #       3333      2018-02-08 01:02:46    0.00303281145170331    bad
+        #       3333      2018-02-08 01:01:42    0.00303281145170331    bad
+        #       3333      2018-02-08 01:00:39    0.00303281145170331    bad
+        #       3333      2018-02-08 00:59:34    0.00303281145170331    bad
+        #       3333      2018-02-08 00:58:31    0.00303281145170331    bad
+        #       3333      2018-02-08 00:57:27    165599.015625          good
+        #       3333      2018-02-08 00:56:23    165599.015625          good
+        #       3333      2018-02-08 00:55:20    165599.015625          good
+        #       3333      2018-02-08 00:54:16    165599.015625          good
+        ################################################################################################################
+        print("Step 2: Processing bad case 1.x")
+        try:
+            cnx_system = mysql.connector.connect(**config.myems_system_db)
+            cursor_system = cnx_system.cursor(dictionary=True)
+
+            query = (" SELECT id, high_limit, low_limit "
+                     " FROM tbl_points "
+                     " WHERE object_type='ENERGY_VALUE'")
+            cursor_system.execute(query)
+            rows_points = cursor_system.fetchall()
+
+            point_dict = dict()
+            if rows_points is not None and len(rows_points) > 0:
+                for row in rows_points:
+                    point_dict[row['id']] = {"high_limit": row['high_limit'],
+                                             "low_limit": row['low_limit']}
+        except Exception as e:
+            logger.error("Error in step 2.1 of clean_energy_value.process " + str(e))
+            time.sleep(60)
+            continue
+        finally:
+            if cursor_system:
+                cursor_system.close()
+            if cnx_system:
+                cnx_system.close()
+
+        try:
+            query = (" SELECT id, point_id, actual_value "
+                     " FROM tbl_energy_value "
+                     " WHERE utc_date_time >= %s AND utc_date_time <= %s AND is_bad IS NOT TRUE ")
+            cursor.execute(query, (min_datetime, max_datetime,))
+            rows_energy_values = cursor.fetchall()
+        except Exception as e:
+            logger.error("Error in step 2.2 of clean_energy_value.process " + str(e))
+            if cursor:
+                cursor.close()
+            if cnx:
+                cnx.close()
+            time.sleep(60)
+            continue
+
+        bad_list = list()
+
+        if rows_energy_values is not None and len(rows_energy_values) > 0:
+            for row_energy_value in rows_energy_values:
+                point_id = row_energy_value[1]
+                actual_value = row_energy_value[2]
+                point = point_dict.get(point_id, None)
+                if point is None or actual_value > point['high_limit'] or actual_value < point['low_limit']:
+                    bad_list.append(row_energy_value[0])
+
+        print('bad list: ' + str(bad_list))
+        if len(bad_list) > 0:
+            try:
+                update = (" UPDATE tbl_energy_value "
+                          " SET is_bad = TRUE "
+                          " WHERE id IN (" + ', '.join(map(str, bad_list)) + ")")
+                cursor.execute(update, )
+                cnx.commit()
+            except Exception as e:
+                logger.error("Error in step 2.3 of clean_energy_value.process " + str(e))
+                if cursor:
+                    cursor.close()
+                if cnx:
+                    cnx.close()
+                time.sleep(60)
+                continue
+
+        ################################################################################################################
+        # Step 3: check bad case class 2 which is in concave shape model.
+        ################################################################################################################
+        print("Step 3: Processing bad case 2.x")
+        ################################################################################################################
+        # bad case 2.1
+        # id    point_id  utc_date_time          actual_value       is_bad (expected)
+        #       3333      2018-02-05 04:55:45    146129.015         good
+        #       3333      2018-02-05 04:56:49    146129.015         good
+        #       3333      2018-02-05 04:57:54    146129.015         good
+        #       3333      2018-02-05 05:22:52    145693.015         bad
+        #       3333      2018-02-05 05:25:01    146274             good
+        #       3333      2018-02-05 05:26:03    146274             good
+        #       3333      2018-02-05 05:27:05    146274             good
+        #       3333      2018-02-05 05:29:30    146274             good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 2.2
+        # id    point_id	utc_date_time	    actual_value	is_bad (expected)
+        #       3321	    2018-05-15 15:09:54	33934040         good
+        #       3321	    2018-05-15 15:08:51	33934040         good
+        #       3321	    2018-05-15 15:07:47	33934040         good
+        #       3321	    2018-05-15 15:06:44	33934040         good
+        #       3321	    2018-05-15 15:05:40	33934040         good
+        #       3321	    2018-05-15 15:04:36	33934040         good
+        #       3321	    2018-05-15 09:09:00	33928880	     bad
+        #       3321	    2018-05-15 09:05:23	33933568         good
+        #       3321	    2018-05-15 09:04:20	33933568         good
+        #       3321	    2018-05-15 09:03:16	33933568         good
+        #       3321	    2018-05-15 09:02:13	33933560         good
+        #       3321	    2018-05-15 09:01:09	33933560         good
+        #       3321	    2018-05-15 09:00:04	33933560         good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 2.3
+        # id    point_id	utc_date_time	    actual_value	is_bad (expected)
+        #       554	        2018-05-19 15:32:52	24001            good
+        #       554	        2018-05-19 15:30:45	24001            good
+        #       554	        2018-05-19 15:28:39	24001            good
+        #       554	        2018-05-19 15:26:32	24001            good
+        #       554	        2018-05-19 15:24:25	24001            good
+        #       554	        2018-05-19 15:22:18	24001            good
+        #       554	        2018-05-19 15:20:10	24001            good
+        #       554	        2018-05-19 15:18:04	24001            good
+        #       554	        2018-05-19 15:15:58	24001            good
+        #       554	        2018-05-19 15:13:51	24001            good
+        #       554	        2018-05-19 15:11:43	24001            good
+        #       554	        2018-05-19 15:09:37	24001            good
+        #       554	        2018-05-19 15:07:29	24000            good
+        #       554	        2018-05-19 15:05:22	23000	         bad
+        #       554	        2018-05-19 15:03:14	23999            good
+        #       554	        2018-05-19 15:01:06	23999            good
+        #       554	        2018-05-19 14:58:59	23999            good
+        #       554	        2018-05-19 14:56:52	23998            good
+        #       554	        2018-05-19 14:54:45	23998            good
+        #       554	        2018-05-19 14:52:39	23998            good
+        ################################################################################################################
+        # todo bad case 2.3.1
+        # "id", "point_id", "utc_date_time", "actual_value", "is_bad" (actual)
+        # 68504700, 2, "2021-01-09 03:40:12.0", 40454414.063, 0
+        # 68507243, 2, "2021-01-09 03:43:12.0", 40454476.563, 0
+        # 68510030, 2, "2021-01-09 03:47:17.0", 40428074.219, 0 ?
+        # 68512573, 2, "2021-01-09 03:50:18.0", 40454621.094, 0
+        # 68515421, 2, "2021-01-09 03:54:23.0", 40454703.125, 0
+        # 68517964, 2, "2021-01-09 03:57:23.0", 40454761.719, 0
+
+        ################################################################################################################
+        # bad case 2.4
+        # id       point_id utc_date_time          actual_value    is_bad (expected)
+        # 104373141 3336    2018-01-30 03:04:12    216463.015625   good
+        # 104373337 3336    2018-01-30 03:05:15    216463.015625   good
+        # 104373555 3336    2018-01-30 03:06:20    216463.015625   good
+        # 104373750 3336    2018-01-30 03:07:25    192368.015625   bad
+        # 104373957 3336    2018-01-30 03:08:29    192368.015625   bad
+        # 104374175 3336    2018-01-30 03:09:33    192368.015625   bad
+        # 104374382 3336    2018-01-30 03:10:38    192368.015625   bad
+        # 104374604 3336    2018-01-30 03:11:42    192368.015625   bad
+        # 104374792 3336    2018-01-30 03:12:47    192368.015625   bad
+        # 104375010 3336    2018-01-30 03:13:51    192368.015625   bad
+        # 104375200 3336    2018-01-30 03:14:55    192368.015625   bad
+        # 104375418 3336    2018-01-30 03:16:00    192368.015625   bad
+        # 104375617 3336    2018-01-30 03:17:04    192368.015625   bad
+        # 104375837 3336    2018-01-30 03:18:08    192368.015625   bad
+        # 104376023 3336    2018-01-30 03:19:12    192368.015625   bad
+        # 104376216 3336    2018-01-30 03:20:16    192368.015625   bad
+        # 104376435 3336    2018-01-30 03:21:21    192368.015625   bad
+        # 104376634 3336    2018-01-30 03:22:25    192368.015625   bad
+        # 104376853 3336    2018-01-30 03:23:30    192368.015625   bad
+        # 104377071 3336    2018-01-30 03:24:34    192368.015625   bad
+        # 104377274 3336    2018-01-30 03:25:38    192368.015625   bad
+        # 104377501 3336    2018-01-30 03:26:42    216574.015625   good
+        # 104377714 3336    2018-01-30 03:27:47    216574.015625   good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 2.5
+        # id       point_id utc_date_time          actual_value  is_bad (expected)
+        # 104370839 3334    2018-01-30 02:52:23    844966.0625   good
+        # 104371064 3334    2018-01-30 02:53:27    844966.0625   good
+        # 104371261 3334    2018-01-30 02:54:32    844966.0625   good
+        # 104371479 3334    2018-01-30 02:55:36    826142.0625   bad
+        # 104371672 3334    2018-01-30 02:56:41    826142.0625   bad
+        # 104371884 3334    2018-01-30 02:57:45    826142.0625   bad
+        # 104372110 3334    2018-01-30 02:58:49    826142.0625   bad
+        # 104372278 3334    2018-01-30 02:59:54    845019.0625   good
+        # 104372512 3334    2018-01-30 03:00:58    845019.0625   good
+        # 104372704 3334    2018-01-30 03:02:03    845019.0625   good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 2.6
+        # 394084273	1001444	2019-08-22 03:39:44	   38969028      good
+        # 394083709	1001444	2019-08-22 03:38:43    38968876	     good
+        # 394083145	1001444	2019-08-22 03:37:43    28371884      bad
+        # 394082019	1001444	2019-08-22 03:35:42    28371884      bad
+        # 394081456	1001444	2019-08-22 03:34:42    28371884      bad
+        # 394080892	1001444	2019-08-22 03:33:42    28371884      bad
+        # 394079200	1001444	2019-08-22 03:30:38    28371884      bad
+        # 394077511	1001444	2019-08-22 03:27:37    38968408	     good
+        # 394076947	1001444	2019-08-22 03:26:37    38968236	     good
+        # 394076384	1001444	2019-08-22 03:25:37    38968060	     good
+        ################################################################################################################
+
+        ################################################################################################################
+        # bad case 2.7
+        # id       point_id utc_date_time          actual_value   is_bad (expected)
+        # 17303260 11       2020-3-15 05:43:52     33600          good
+        # 17303399 11       2020-3-15 05:44:58     33600          good
+        # 17303538 11       2020-3-15 05:46:04     33600          good
+        # 17303677 11       2020-3-15 05:47:10     33500          bad
+        # 17303816 11       2020-3-15 05:48:15     33500          bad
+        # 17303955 11       2020-3-15 05:49:21     33600          good
+        # 17304094 11       2020-3-15 05:50:27     33600          good
+        # 17304233 11       2020-3-15 05:51:33     33600          good
+        ################################################################################################################
+
+        try:
+            query = (" SELECT point_id, id, utc_date_time, actual_value "
+                     " FROM tbl_energy_value "
+                     " WHERE utc_date_time >= %s AND utc_date_time <= %s AND is_bad IS NOT TRUE "
+                     " ORDER BY point_id, utc_date_time ")
+            cursor.execute(query, (min_datetime, max_datetime,))
+            rows_energy_values = cursor.fetchall()
+        except Exception as e:
+            logger.error("Error in step 3.1 of clean_energy_value.process " + str(e))
+            if cursor:
+                cursor.close()
+            if cnx:
+                cnx.close()
+            time.sleep(60)
+            continue
+
+        point_value_dict = dict()
+        current_point_value_list = list()
+        current_point_id = 0
+
+        if rows_energy_values is not None and len(rows_energy_values) > 0:
+            for row_energy_value in rows_energy_values:
+                previous_point_id = current_point_id
+                current_point_id = row_energy_value[0]
+                if current_point_id not in point_value_dict.keys():
+                    # new point id found
+                    # save previous point values
+                    if len(current_point_value_list) > 0:
+                        point_value_dict[previous_point_id] = current_point_value_list
+                        current_point_value_list = list()
+
+                    current_point_value_list.append({'id': row_energy_value[1],
+                                                     'actual_value': row_energy_value[3]})
+                else:
+                    current_point_value_list.append({'id': row_energy_value[1],
+                                                     'actual_value': row_energy_value[3]})
+            # end of for loop
+            # save rest point values
+            if len(current_point_value_list) > 0:
+                point_value_dict[current_point_id] = current_point_value_list
+
+        bad_list = list()
+
+        for point_id, point_value_list in point_value_dict.items():
+            if len(point_value_list) <= 1:
+                continue
+            elif len(point_value_list) == 2:
+                if point_value_list[1]['actual_value'] < point_value_list[0]['actual_value']:
+                    bad_list.append(point_value_list[1]['id'])
+                continue
+            else:
+                base_point_value = point_value_list[0]['actual_value']
+                concave_point_value_list = list()
+                for i in range(len(point_value_list)):
+                    if point_value_list[i]['actual_value'] < base_point_value:
+                        # candidate concave value found
+                        concave_point_value_list.append(point_value_list[i]['id'])
+                    else:
+                        # normal value found
+                        if len(concave_point_value_list) > 0:
+                            # save confirmed concave value(s) to bad value(s)
+                            bad_list.extend(concave_point_value_list)
+
+                        # prepare for next candidate concave value list
+                        base_point_value = point_value_list[i]['actual_value']
+                        concave_point_value_list.clear()
+                continue
+
+        print('bad list: ' + str(bad_list))
+        if len(bad_list) > 0:
+            try:
+                update = (" UPDATE tbl_energy_value "
+                          " SET is_bad = TRUE "
+                          " WHERE id IN (" + ', '.join(map(str, bad_list)) + ")")
+                cursor.execute(update, )
+                cnx.commit()
+            except Exception as e:
+                logger.error("Error in step 3.2 of clean_energy_value.process " + str(e))
+                if cursor:
+                    cursor.close()
+                if cnx:
+                    cnx.close()
+                time.sleep(60)
+                continue
+
+        ################################################################################################################
+        # TODO: bad case 2.8
+        # id          point_id utc_date_time          actual_value is_bad (expected)
+        # 105752070    3333    2018-02-04 00:27:15    138144       good
+        # 105752305    3333    2018-02-04 00:28:19    138144       good
+        # 105752523    3333    2018-02-04 00:29:22    138144       good
+        # 105752704    3333    2018-02-04 00:30:26    138144       good
+        # 105752924    3333    2018-02-04 00:31:30    138144       good
+        # 105753138    3333    2018-02-04 00:32:34    138144       good
+        # 105753351    3333    2018-02-04 00:33:38    138144       good
+        # 105753577    3333    2018-02-04 00:34:42    52776558592  bad?
+        # 105753794    3333    2018-02-04 00:35:46    52776558592  bad?
+        # 105753999    3333    2018-02-04 00:36:50    52776558592  bad?
+        # 105754231    3333    2018-02-04 00:37:54    52776558592  bad?
+        # 105754443    3333    2018-02-04 00:38:58    52776558592  bad?
+        # 105754655    3333    2018-02-04 00:40:01    52776558592  bad?
+        # 105754878    3333    2018-02-04 00:41:06    52776558592  bad?
+        # 105755092    3333    2018-02-04 00:42:09    52776558592  bad?
+        # 105755273    3333    2018-02-04 00:43:14    52776558592  bad?
+        # 105755495    3333    2018-02-04 00:44:17    52776558592  bad?
+        # 105755655    3333    2018-02-04 00:45:21    52776558592  bad?
+        # 105755854    3333    2018-02-04 00:46:25    52776558592  bad?
+        # 105756073    3333    2018-02-04 00:47:29    52776558592  bad?
+        # 105756272    3333    2018-02-04 00:48:34    52776558592  bad?
+        # 105756489    3333    2018-02-04 00:49:38    52776558592  bad?
+        ################################################################################################################
+
+        ################################################################################################################
+        # TODO: bad case 2.10
+        # id       point_id utc_date_time          actual_value   is_bad (expected)
+        # 106363135 3336    2018-02-06 04:45:57    253079.015625  good
+        # 106363776 3336    2018-02-06 04:49:09    253079.015625  good
+        # 106364381 3336    2018-02-06 04:52:21    253079.015625  good
+        # 106364603 3336    2018-02-06 04:53:25    253079.015625  good
+        # 106365213 3336    2018-02-06 04:56:37    253079.015625  good
+        # 106365634 3336    2018-02-06 04:58:45    253079.015625  good
+        # 106366055 3336    2018-02-06 05:00:53    253079.015625  good
+        # 106367097 3336    2018-02-06 05:06:12    259783.015625  bad?
+        # 106367507 3336    2018-02-06 05:08:21    259783.015625  bad?
+        # 106368318 3336    2018-02-06 05:12:37    259783.015625  bad?
+        # 106368732 3336    2018-02-06 05:14:44    259783.015625  bad?
+        # 106368952 3336    2018-02-06 05:15:48    259783.015625  bad?
+        # 106369145 3336    2018-02-06 05:16:52    259783.015625  bad?
+        # 106369353 3336    2018-02-06 05:17:56    259783.015625  bad?
+        ################################################################################################################
+
+        ################################################################################################################
+        # TODO: bad case 2.11
+        # id       point_id utc_date_time          actual_value   is_bad (expected)
+        # 14784589 21	    2020-03-05 07:22:22    17990           good
+        # 14784450 21	    2020-03-05 07:21:17    17990           good
+        # 14784311 21	    2020-03-05 07:20:10    17990           good
+        # 14784172 21	    2020-03-05 07:19:04    17990           good
+        # 14784033 21	    2020-03-05 07:17:58    18990           bad
+        # 14783894 21	    2020-03-05 07:16:52    17990           good
+        # 14783755 21	    2020-03-05 07:15:46    17990           good
+        # 14783616 21	    2020-03-05 07:14:40    17990           good
+        # 14783477 21	    2020-03-05 07:13:34    17990           good
+        # 14783338 21	    2020-03-05 07:12:28    17990           good
+        # 14783199 21	    2020-03-05 07:11:22    17990           good
+        ################################################################################################################
+
+        ################################################################################################################
+        # TODO: bad case 2.12
+        # id       point_id utc_date_time          actual_value   is_bad (expected)
+        # 3337308  21       2020-01-07 09:02:18    7990           good
+        # 3337174  21       2020-01-07 09:01:13    7990	          good
+        # 3337040  21       2020-01-07 09:00:08    7990	          good
+        # 3336906  21       2020-01-07 08:59:04    7990	          good
+        # 3336772  21       2020-01-07 08:57:59    7990	          good
+        # 3336638  21       2020-01-07 08:56:54    8990	          bad
+        # 3336504  21       2020-01-07 08:55:49    7990	          good
+        # 3336370  21       2020-01-07 08:54:44    7990	          good
+        # 3336236  21       2020-01-07 08:53:39    7990	          good
+        # 3336102  21       2020-01-07 08:52:34    7990	          good
+        # 3335968  21       2020-01-07 08:51:30    7990	          good
+        ################################################################################################################
+        # Step 4: tag the is_bad property of energy values.
+        ################################################################################################################
+        try:
+            update = (" UPDATE tbl_energy_value "
+                      " SET is_bad = FALSE "
+                      " WHERE utc_date_time >= %s AND utc_date_time < %s AND is_bad IS NULL ")
+            # NOTE: use '<' instead of '<=' in WHERE statement because there may be some new inserted values
+            cursor.execute(update, (min_datetime, max_datetime,))
+            cnx.commit()
+        except Exception as e:
+            logger.error("Error in step 4 of clean_energy_value.process " + str(e))
+            time.sleep(60)
+            continue
+        finally:
+            if cursor:
+                cursor.close()
+            if cnx:
+                cnx.close()
+
+        time.sleep(900)
diff --git a/myems-cleaning/config.py b/myems-cleaning/config.py
new file mode 100644
index 00000000..8cbaf1d9
--- /dev/null
+++ b/myems-cleaning/config.py
@@ -0,0 +1,24 @@
+# definition of the database
+myems_system_db = {
+    'user': 'root',
+    'password': '!MyEMS1',
+    'host': '127.0.0.1',
+    'database': 'myems_system_db',
+    'port': 3306,
+}
+
+myems_historical_db = {
+    'user': 'root',
+    'password': '!MyEMS1',
+    'host': '127.0.0.1',
+    'database': 'myems_historical_db',
+    'port': 3306,
+}
+
+# indicates how long analog values and digital values will be kept in database
+# the longer days the more memory and disc space needed.
+live_in_days = 365
+# note: By default, energy values in historical db will never be deleted automatically.
+
+# indicates if the program is in debug mode
+is_debug = False
diff --git a/myems-cleaning/main.py b/myems-cleaning/main.py
new file mode 100644
index 00000000..391461ed
--- /dev/null
+++ b/myems-cleaning/main.py
@@ -0,0 +1,37 @@
+import logging
+from logging.handlers import RotatingFileHandler
+from multiprocessing import Process
+import clean_analog_value
+import clean_digital_value
+import clean_energy_value
+
+
+def main():
+    """main"""
+    # create logger
+    logger = logging.getLogger('myems-cleaning')
+    # specifies the lowest-severity log message a logger will handle,
+    # where debug is the lowest built-in severity level and critical is the highest built-in severity.
+    # For example, if the severity level is INFO, the logger will handle only INFO, WARNING, ERROR, and CRITICAL
+    # messages and will ignore DEBUG messages.
+    logger.setLevel(logging.ERROR)
+    # create file handler which logs messages
+    fh = RotatingFileHandler('myems-cleaning.log', maxBytes=1024*1024, backupCount=1)
+    # create formatter and add it to the handlers
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    fh.setFormatter(formatter)
+    # add the handlers to logger
+    logger.addHandler(fh)
+
+    # clean analog values
+    Process(target=clean_analog_value.process, args=(logger,)).start()
+    # clean digital values
+    Process(target=clean_digital_value.process, args=(logger,)).start()
+    # clean energy values
+    Process(target=clean_energy_value.process, args=(logger,)).start()
+
+
+if __name__ == '__main__':
+    main()
+
+
diff --git a/myems-cleaning/myems-cleaning.service b/myems-cleaning/myems-cleaning.service
new file mode 100644
index 00000000..d1acc4ba
--- /dev/null
+++ b/myems-cleaning/myems-cleaning.service
@@ -0,0 +1,15 @@
+[Unit]
+Description=myems-cleaning daemon
+After=network.target
+
+[Service]
+User=root
+Group=root
+ExecStart=/usr/bin/python3 /myems-cleaning/main.py
+ExecReload=/bin/kill -s HUP $MAINPID
+ExecStop=/bin/kill -s TERM $MAINPID
+PrivateTmp=true
+Restart=always
+
+[Install]
+WantedBy=multi-user.target
\ No newline at end of file
diff --git a/myems-modbus-tcp/LICENSE b/myems-modbus-tcp/LICENSE
index c6cedd28..b91c1ac4 100644
--- a/myems-modbus-tcp/LICENSE
+++ b/myems-modbus-tcp/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 MyEMS
+Copyright (c) 2021 MyEMS
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal