Electricity Bill Forecasting Application by Home Energy Monitoring System Charnon Chupong
Boonyang Plangklang
Dept. of Electrical Engineering Faculty of Engineering, RMUTT Pathumthani, Thailand
[email protected]
Dept. of Electrical Engineering Faculty of Engineering, RMUTT Pathumthani, Thailand
[email protected]
Abstract— Home energy monitoring system has importance rule in home energy management. Many reports show that it has effectiveness for reducing energy consumption in home. But in medium term and long term of using home energy monitoring system there are some report show the rapidly dismiss of energy saving effective because of user do not pay attention anymore. For improve the traditional home energy monitoring system there are three concepts should be applied to the systems, 1) the system must be a learning tool not just a monitoring tool, 2) the system should be tailored made for individual users and 3) users should use less effort to dealing with the system. This article applied these concepts to home energy monitoring system and create an application to forecasting the user’s electricity bill. The system has applications programming interface (API) that allow users to create applications upon their requirements. From API we have create an application to forecasting the user’s electricity bill that report to user via email daily, user have less effort to receive and translate the information. And from that daily report user can learn of how their behaviors or their measures effect the electricity cost. The accuracy of electricity bill forecasting application was tested by comparing the forecast cost and actual cost and found 96% of accuracy, the result is highly acceptable. Keywords—home energy forecasting; smart meter
monitoring;
electricity
bill
I. INTRODUCTION In Thailand over last 5 years, residential sector has the highest growth rate of electrical energy consumption [1] even higher than industrial sector. The high growth rate of electrical energy consumption in residential sector may cause from the growth of real-estate business and inappropriate energy management measures in residential sector. From our review, home’s energy monitoring system has importance rule in home energy management system. There are some reports show the effective of home’s energy monitoring can reduce energy consumption about 5-15% [2], [3]. But in medium term and long term the effect of home’s energy monitoring will diminish rapidly from weeks to four months [4], [5] because users do not pay any attention anymore. Researcher have acknowledged this problem and study, how to improve the effect of home’s energy monitoring to be sustained over time. Froehlich, J., Findlater, L. and Landay, J. suggest that the home’s energy monitoring must be designed as a learning tool which allows user to learning about energy 978-1-5090-4666-9/17/$31.00 ©2017 IEEE
management by experimentation [6] not just display the data. Kathryn B., Riccardo R., and Ben A. suggest that, the home’s energy monitoring system should be tailor designed to the specified requirements of individual users and should be reduce the user’s effort to dealing with system, perhaps by notify consumers to abnormal usage or specifying which appliances are on or providing some recommendation.[3] Ambient Energy Orb is a frosted glass ball that provides real-time data about energy consumption that apply eco-feedback concept by changing their color according to electricity demand and pricing allow uses to know their energy consumption situation without any effort [7]. Somchai B. and Boonyang P. present the non-intrusive appliances load monitoring (NILM) that allow users to know the consumption of significant appliances without install metering device directly to it. [8] In this article we present the Energy Monitoring System that connected to the internet via Wi-Fi and we provide Application Programing Interface (API) allow any user to create their own user interface, dashboard or application that specified to their individual requirement. And we use this API to create Electricity Bill Forecasting Application that can forecasting the electricity bill and update forecasting value to users via email every day. This application allows users to deal with energy consumption information without any effort. Users can know and learn about effect of their behavior to their electricity bill with forecasting data that provided to them daily. The rest of this article is organized in 3 section, 2nd section is present detail about proposed system the 3rd section is experiment and result and 4th section is conclusion.
Fig.1 Ambient Energy Orb
II. PROPOSED SYSTEM A. Overall System Internet-Enabled Energy Monitoring System consist of some sub-system as Fig.2
Fig.2 System’s Block Diagram • Hardware – We use Multi-function energy meter with Modbus RTU communication port connected to ESP8266 and some ICs to convert Modus RTU protocol to Wi-Fi signal and send data to server with sampling rate 1 minute. • Server – We use cloud service to install database and write service software that access and write database with data send by hardware all service software written by Node JS language. And we write Application Programing Interface (API) service by Node JS language for allow user to get data from database and create their own application. • Database – We use MongoDB a non-SQL type database because it’s fast and more flexible than traditional SQL type database. Data store in database is an object one object represent on sampling data with key, value pair. • Client device – Any device with internet browser can access data via the internet.
B. Application Programing Interface (API) Our Application Programing Interface (API) is a service that allow users to get data from our database to use in their applications as they require without direct access to the database. For more flexibility to use in any platform we use the concept of REST HTTP for our API, user just use GET method with some query string to appropriate URI to receive their required data. The data will return in array of object format. Here is our API list and example of usage in Fig.4. • now, get the most recent data from database. • trend, get n most recent data that user can specified the value of n. • range, get data from date-time1 to date-time2 that user can specify the value of date-time1 and date-time2. • today get all today data • 24hr get all data within last 24 hour • cost/tariff112 calculate electricity cost with tariff 1.1.2 Home, Normal rate • cost/tariff212 calculate electricity cost with tariff 2.1.2 Small Business, Normal Rate • cost/fixed calculate electricity cost with some fixed tariff that user can specified
Fig.4 Example of output from calling API
Fig.3 System’s Hardware install at home
C. Electricity Bill Forecasting Application By API discuss above we create application that can forecast user electricity bill. The concept of this application is using average daily energy consumption in last 7 days to predict energy consumption until billing date and also
calculate electricity cost at due date as flow chart in Fig.5 and then send forecasting value to user via email and popular messaging application like LINE Application. This forecasting process run daily repeat.
Fig.6 Email message and LINE Notification that send to user III. EXPERIMENTS AND RESULTS
Fig.5 Application’s flow chart
A. Data avalibility We have tested the communication ability of system by install it at home, let it measure electricity parameter and send data via Wi-Fi to the internet every 1 minute. We have tested the system for 40 days, if the system work perfectly it should have 57,600 records in database during this test. After 40 days we have checked data in database and found only 50,124 records available that mean 87% of data available or data loss 13%. The clause of this data loss is come from Wi-Fi Signal lost due to electricity outage and hardware malfunction.
B. Accuracy of Forecasting We have tested the Electricity Bill Forecasting Application as discussed in section 2 by compare daily forecast electricity cost each 10 days with actual electricity cost in electricity bill. The result show average accuracy of forecasting is 96%, maximum accuracy is 98% and minimum accuracy is 94% as shown in Fig.7 and Fig.8. The clause of forecasting error is come from due date in application have set to date 21st 0:00 AM of each month but actually due date is upon utility’s staff schedule some month is date 20th and some month is date 21th.
designed (or have potential to tailored designed) to specified individual requirement of users, third the system should let users to dealing with no or less effort. This article presents the home energy monitoring system and its application that applied these three concepts. The proposed system has API service that allow users to create any application upon their requirement as second concept. From proposed system and its API, we create application that can forecast the electricity bill of user with accuracy 96% and send the result to them via email every day that allow the use to have less effort to receive and translate information as third concept. Daily report from proposed application let user to learning the effect of their behavior or their measures to electricity bill quickly, as first concept. Future work, we will test the effect of energy saving from this concepts of energy monitoring system in medium term and long term with larger sampling size. ACKNOWLEDGEMENT This research was supported by Rajamankala University of Technology Thanyaburi Research Fund. for Academic year 2017. REFERENCES
Fig 7. Forecast in each date compare with actual [1]
Department of Development Alternative Energy and Efficiency,Ministry of Energy of Thailand, “Energy Balance of Thailand 2015”, pp.51
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D. S. Parker, “Pilot Evaluation of Energy Savings from Residential Energy Demand Feedback Devices,” Florida, 2008
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S. S. van Dam, C. a. Bakker, and J. D. M. van Hal, “Home energy monitors: impact over the medium-term,” Build. Res. Inf., vol. 38, no. 5, pp. 458–469, Oct. 2010 [5] M. a. Alahmad, P. G. Wheeler, A. Schwer, J. Eiden, and A. Brumbaugh, “A Comparative Study of Three Feedback Devices for Residential Real-Time Energy Monitoring,” IEEE Trans. Ind. Electron., vol. 59, no. 4, pp. 2002–2013, Apr. 2012 [4]
Fig.8 Accuracy of Forecasting IV. CONCLUSION AND FUTURE WORK There are some reports show that the energy saving effect from home energy monitoring system is dismiss very fast in medium term and long term running of system. And from our literature review we can conclude the way to improve the energy monitoring system. The system should be applied three concepts, first the system must be a leaning tool not just a monitoring system, second the system should be tailored
[6]
[7] [8]
Froehlich, J. , Findlater, L. , Landay, J. , The design of eco-feedback technology. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 1999–2008, 2010 Energy Device – Energy Orb., last accessed Jan. 6, 2017. [Online]. Available: http://www.ambientdevices.com/about/energy-devices S. Biansoongnern and B. Plangklang, "Nonintrusive load monitoring (NILM) using an Artificial Neural Network in embedded system with low sampling rate," 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, 2016, pp. 1-4.
