The Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency
DOI:
https://doi.org/10.55003/ETH.410403Keywords:
real-time electricity calculator system, Machine learning, electrical energy consumption, k-Nearest Neighbors model.Abstract
The development of the electric meter system described in this article is titled as Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency. The primary objective is to improve household energy management through prediction and alerts regarding energy usage. The system is designed to collect electricity usage data and various environmental parameters from households, and it employs five machine learning models to identify the best model for this purpose. The chosen model, Support Vector Regression (SVR), is used to predict energy consumption. In the research methodology, the system records real-time energy usage data into a CSV file. The predictive features include temperature, number of occupants, house size, and appliance usage. This data is standardized before being used to train the SVR model. After training, the model’s predictions are evaluated using the Root Mean Square Error (RMSE). The experimental results show that the SVR model effectively predicts electricity consumption, with a normalized RMSE of 57.56 and a cross-validation RMSE of 58.38, indicating the model’s accuracy. The visualizations provide a clear understanding of the overall relationship between actual and predicted values. Household electricity usage prediction enables users to plan energy consumption more efficiently, potentially reducing costs and improving energy efficiency. The development of this system can be applied in various fields, including industry and agriculture, to promote energy conservation and reduce environmental impact.
References
S. Ndaba and I. E. Davidson, “The Implementation of Smart Meters for Electric Grid Improvements and Reliable Power Flow Data on Electrical Power Distribution Network,” 2020 IEEE PES/IAS PowerAfrica, Nairobi, Kenya, 2020, pp. 1–5, doi: 10.1109/PowerAfrica49420.2020.9219916.
R. C. R, V. G, P. Jeyanthi, S. Revathy, L. M. Gladance and A. V. A. Mary, “Prediction of Electricity Bill using Supervised Machine Learning Technique,” in 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, Apr. 28–30, 2022, pp. 1232–1236, doi: 10.1109/ICOEI53556.2022.9777232.
Z. Yan and H. Wen, “Electricity Theft Detection Base on Extreme Gradient Boosting in AMI,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2021, Art no. 2504909, doi: 10.1109/TIM.2020.3048784.
G. Wiczyński and P. Kuwałek, “Voltage Distortion Influence on Flicker Severity Measurement by AMI Energy Meters,” IEEE Transactions on Industrial Electronics, vol. 69, no. 10, pp. 10684–10693, Oct. 2022, doi: 10.1109/TIE.2021.3120465.
R. Qi, J. Zheng, Z. Luo and Q. Li, “A Novel Unsupervised Data-Driven Method for Electricity Theft Detection in AMI Using Observer Meters,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022, doi: 10.1109/TIM.2022.3189748.
M. J. Abdulaal, M. I. Ibrahem, M. M. E. A. Mahmoud, J. Khalid, A. J. Aljohani, A. H. Milyani and A. M. Abusorrah, “Real-Time Detection of False Readings in Smart Grid AMI Using Deep and Ensemble Learning,” IEEE Access, vol. 10, pp. 47541–47556, 2022, doi: 10.1109/ACCESS.2022.3171262.
J. L. Gallardo, M. A. Ahmed and N. Jara, “LoRa IoT-Based Architecture for Advanced Metering Infrastructure in Residential Smart Grid,” IEEE Access, vol. 9, pp. 124295–124312, 2021, doi: 10.1109/ACCESS.2021.3110873.
B. S. Sami, “A Survey of Hydrogen Energy and I-Energy Applications: Household Intelligent Electrical Power Systems,” IEEE Access, vol. 8, pp. 55181–55203, 2020, doi: 10.1109/ACCESS.2020.2981349.
G. Mehta, R. Khanam and V. K. Yadav, “A Novel IoT based Smart Energy Meter for Residential Energy Management in Smart Grid Infrastructure,” in 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, Aug. 26–27, 2021, pp. 47–52, doi: 10.1109/SPIN52536.2021.9566032.
M. Güçyetmez, H. S. Farhan, “Enhancing smart grids with a new IOT and cloud-based smart meter to predict the energy consumption with time series,” Alexandria Engineering Journal, vol.79, pp.44–55, 2023, doi: 10.1016/j.aej.2023.07.071.
J. M. J, V. D. A. C, C. R. S and A. E. Xavier, “Design and development of IoT based Gateway for Advanced Metering Infrastructure in an Educational Institution,” in 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India, 2022, pp. 824–829, doi: 10.1109/ICICICT54557.2022.9917582.
K. V. Jyothi Prakash, N. S. Chethana, F. Tamkeen, C. S. Kala and N. R. Kavya, “Designing of Microcontroller based Energy Meter (Smart Energy Meter) for Energy Preserving,” in 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2019, pp. 1252-1255, doi: 10.1109/ICCES45898.2019.9002590.
J. Breitenbach et al., “A Systematic Literature Review of Deep Learning Approaches in Smart Meter Data Analytics,” in 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Los Alamitos, CA, USA, 2022, pp. 1337–1342, doi: 10.1109/COMPSAC54236.2022.00211.
D. A. Bashawyah and S. M. Qaisar, “Machine Learning Based Short-Term Load Forecasting for Smart Meter Energy Consumption Data in London Households,” in 2021 IEEE 12th International Conference on Electronics and Information Technologies (ELIT), Lviv, Ukraine, 2021, pp. 99–102, doi: 10.1109/ELIT53502.2021.9501104.
A. Z. Lin and A. James, “Using Smart Meter Data and Machine Learning to Identify Residential Light-duty Electric Vehicles,” in 2022 IEEE Conference on Technologies for Sustainability (SusTech), Corona, CA, USA, 2022, pp. 245–251, doi: 10.1109/SusTech53338.2022.9794221.
P. Chandel and B. Sawle, “Cyber Security of Smart Metering Infrastructure Using Hybrid Machine Learning Technique,” in 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1–7, doi: 10.1109/ISCON57294.2023.10112175.
B. Chaudhari, N. Shinde, G. Yelave, G. Sonawane and K. Borse, “Real-Time Electricity Bill Management System Using IoT,” in 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1–5, doi: 10.1109/ESCI56872.2023.10099628.
N. Latha., B. V. Divya, U. Surendra and N. V. Archana, “Micro grid Communication Technologies: An Overview,” in 2022 IEEE Industrial Electronics and Applications Conference (IEACon), Kuala Lumpur, Malaysia, 2022, pp. 49–54, doi: 10.1109/IEACon55029.2022.9951841.
Energy Consumption Management, Office of the National Energy Policy Council, Bangkok, Thailand, 2002, pp.1–10.
M. Bhandarkar, A. Suryawanshi, S. Deoghare, N. Isaac, A. Hegu and P. Wankhede, “Electricity Billing and Consumption Prediction using Machine Learning,” in 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India, 2023, pp. 1–4, doi: 10.1109/ICCUBEA58933.2023.10392239.
R. Verma, R. Gupta, A. Sahu and N. Goyal, “Study on Energy Consumption Forecasting Using Machine Learning”, International Journal of Innovative Research in Computer Science and Technology, vol. 12, pp. 389–394, 2024, doi: 10.55524/csistw.2024.12.1.68.
L. S. B. Pereira, R. N. Rodrigues and E. A. C. A. Neto, “Modeling of Energy Management Systems using Artificial Intelligence,” in 2020 IEEE International Systems Conference (SysCon), Montreal, QC, Canada, 2020, pp. 1–6, doi:10.1109/SysCon47679.2020.9275831.
M. Atanasovski, M. Kostov, B. Arapinoski and M. Spirovski, “K-Nearest Neighbor Regression for Forecasting Electricity Demand,” in 2020 55th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Niš, Serbia, Sep. 10–12, 2020, pp. 110–113, doi: 10.1109/ICEST49890.2020.9232768.
G. V. Reddy, L. J. Aitha, Ch. Poojitha, A. N. Shreya, D. K. Reddy and G. S. Meghana, “Electricity Consumption Prediction Using Machine Learning,” in 4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023), 2023, pp. 1–9, doi: 10.1051/e3sconf/202339101048.
S. Aimal, N. Javaid, T. Islam, W. Z. Khan, M. Y. Aalsalem and H. Sajjad, “An Efficient CNN and KNN Data Analytics for Electricity Load Forecasting in the Smart Grid,” Web, Artificial Intelligence and Network Applications, vol. 927, pp. 592–603, 2019, doi: 10.1007/978-3-030-15035-8_57.
T. Ashfaq and N. Javaid, “Short-Term Electricity Load and Price Forecasting using Enhanced KNN,” in 2019 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2019, pp. 266–2665, doi: 10.1109/FIT47737.2019.00057.
F. H. Al-Qahtani and S. F. Crone, “Multivariate k-nearest neighbour regression for time series data — A novel algorithm for forecasting UK electricity demand,” in The 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, 2013, pp. 1–8, doi: 10.1109/IJCNN.2013.6706742.
A. Shiralkar, H. Kulkarni, P. Mane, and S. Bakre, “Machine Learning Based Decision Trees for Energy Meter Inspection in Power Sector,” International Journal of Electrical and Electronics Engineering, vol. 11, no. 6, p. 130–135, 2024, doi: 10.14445/23488379/IJEEE-V11I6P114.
O. Nooruldeen, M. R. Baker, A.M. Aleesa, A. Ghareeb, E. H. Shaker, “Strategies for predictive power: Machine learning models in city-scale load forecasting,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 6, 2023, Art. no. 100392, doi: 10.1016/j.prime.2023.100392.
A. Fan, Y. Wang, L. Yang, X. Tu, J. Yang, Y. Shu, “Comprehensive evaluation of machine learning models for predicting ship energy consumption based on onboard sensor data,” Ocean and Coastal Management, vol. 248, 2024, Art. no. 106946, doi: 10.1016/j.ocecoaman.2023.106946.
A. I. Satriananda, L. Kamelia, M. R. Efendi and A. Kusnawan, “The Prototype of Smart Power Meter at Home Based on Internet of Things,” in 2021 7th International Conference on Wireless and Telematics (ICWT), Bandung, Indonesia, 2021, pp. 1–3, doi: 10.1109/ICWT52862.2021.9678480.
A. -W. O. Muhammed, V. Oisamoje, H. E. Amhenrior, E. M. J. Evbogbai, V. K. Abanihi and L. O. Bello, “Design and Implementation of an IoT Based Home Energy Monitoring System,” in 2022 5th Information Technology for Education and Development (ITED), Abuja, Nigeria, 2022, pp. 1–7, doi: 10.1109/ITED56637.2022.10051192.
T. B. Nguyen and T. C. Nguyen, “Design and fabrication of an IoT-based smart electrical meter for residential energy management,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 30, no. 3, pp.1259–268, 2023, doi: 10.11591/ijeecs.v30.i3.pp1259-1268.
G. A. López-Alfaro, L. Á. Hernández-Fernández, J. A. Aguirre-Núñez, J. P. Serrano-Rubio, R. Herrera-Guzmán and L. M. Rodríguez-Vidal, “Smart IoT Device for Energy Consumption Monitoring in Real Time,” in 2021 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico, 2021, pp. 1–6 doi: 10.1109/ROPEC53248.2021.9668181.
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