International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 16, No. 6, 8 Dec. 2024
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A Smart System for Monthly Electrical Energy Consumption Prediction Using Machine Learning
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Author(s)
Index Terms
Energy Consumption, Machine Learning, Mobile Application, Regression, Cost Estimation, and Predictive Modeling
Abstract
Forecasting electrical energy consumption is becoming increasingly important for a country's citizens as it addresses rising energy demand and energy waste issues. A useful electrical energy consumption prediction scheme could help users estimate their monthly electricity bills and the use of new electrical appliances in their homes. Traditional energy consumption prediction methods are time-consuming and necessitate expert assistance to analyze and calculate energy use over time. The limitations of the existing works are that the existing literature does not accurately predict monthly energy consumption and costs using machine learning. They concentrated on electrical energy consumption over a short period of time in a single building, using seasonal data rather than automating the system for repeated use. To address these issues, this paper proposes machine learning-based automation systems that predict monthly energy consumption, estimate costs, and identify relevant features using data from electrical home appliances in Bangladesh. Several regression models, including Random Forest, Decision Tree, XGBoost, Boosting, and LightGBM Regressor, are tested to find the best prediction model. We have performed dataset collection, dataset cleaning, feature extraction, scaling, normalization, hyper parameter tuning, training, testing, and model selection activities. The simulation results clearly indicated that the Random Forest regressor model performed better than the other models, with higher R squared values and lower error values. The comparison results revealed that the proposed random forest regression model outperforms previous works by at least 4% in accuracy and 7% in mean absolute error. The proposed mobile application helps users make informed decisions by calculating energy consumption for new home appliances, making recommendations, delivering updated news from the power board, and providing required guidelines. The mobile application feature evaluation results revealed that our proposed application received an excellent rating from more than 70% of customers.
Cite This Paper
Md. Shimul Mahmud, Mahfuzul H. Chowdhury, "A Smart System for Monthly Electrical Energy Consumption Prediction Using Machine Learning", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.16, No.6, pp. 42-61, 2024. DOI:10.5815/ijieeb.2024.06.04
Reference
[1]V. R. Chifu et al., “Deep learning for forecasting the energy consumption in public buildings,” 20th RoEduNet Conference: Networking in Education and Research (RoEduNet), Iasi, Romania, 2021, pp. 1-6.
[2]W. Li et al., “Office Building Energy Consumption Prediction Based on LSTM and SVR,” IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), Chongqing City, China, 2020, pp. 171-176.
[3]Power Division of Bangladesh Govt., “Energy Efficiency and Conservation Master Plan Up to 2030,” https://elibrary.sreda.gov.bd/public/admin/files/books_202104281990004944.pdf, 2016, pp. 1-126.
[4]Finance Division of Bangladesh Govt., “Power and Energy,” https://mof.portal.gov.bd/, Bangladesh Economic Review, Chapter Ten, pp. 1-20, 2023.
[5]Bangladesh Power Development Board, “Annual Report 2022-23,” https://bpdb.portal.gov.bd/sites/default/files/files/bpdb.portal.gov.bd/annual_reports/, 2023, pp. 1-98.
[6]B. Fan et al., “Intelligent prediction method of building energy consumption based on deep learning, ” Scientific Programming, vol. 2021, no. xx, pp. 1– 9, 2021.
[7]J. Runge et al., “Forecasting energy use in buildings using artificial neural networks: A review,” Energies, MDPI, vol. 12, no. 17, pp. 1–27.
[8]M. Injadat et al., “Machine learning towards intelligent systems: Applications, challenges, and opportunities,” Artificial Intelligence Review, vol. 54, no. 5, pp. 3299–3348, Jan. 2021.
[9]P. Nie et al., “Prediction of home energy consumption based on gradient boosting regression tree,” Energy Reports, vol. 7, no. xx, pp. 1246–1255, 2021.
[10]P. G. Narh and R. Ranjan, “Integration of Ai with mobile application development, ” in 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp. 2605–2611.
[11]H. Tian, K. Li and B. Meng, “A novel prediction modeling scheme based on multiple information fusion for day-ahead electricity price, ” in Chinese Control and Decision Conference (CCDC), 2011, pp. 1801–1805.
[12]N. Kumar et al., “Market clearing price prediction using ann in indian electricity markets, ” in 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), 2016, pp. 454–458.
[13]S. Orenc, E. Acar and M. S. Özerdem, “The electricity price prediction of victoria city based on various regression algorithms,” in Global Energy Conference (GEC), 2022, pp. 164–167.
[14]M. Faiq et al., “Prediction of energy consumption in campus buildings using long short-term memory, ” Alexandria Engineering Journal, vol. 67, no. 01, pp. 65– 76, 2023.
[15]R. Olu-Ajayi and H. Alaka, “Building energy consumption prediction using deep learning, ” in Environmental Design and Management Conference (EDMIC), pp. 1– 6, 2021.
[16]S. Emshagin, W. K. Halim and R. Kashef, “Short-term prediction of household electricity consumption using customized lstm and gru models, ” arXiv preprint, arXiv:2212.08757, pp. 1– 11, 2022.
[17]E. Yorat, K. Zor, N. S. Özbek and L. Sarbulut, “Day-ahead electricity price forecasting using artificial intelligence-based algorithms, ” in International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2023, pp. 121–126.
[18]A.-D. Pham, N.-T. Ngo, T. T. H. Truong, N.-T. Huynh and N.-S. Truong, “Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability, ” Journal of Cleaner Production, vol. 260, no. xx, pp. 1-10, 2020.
[19]H. Deng et al., “Electricity price prediction based on LSTM and Lightgbm, ” in 4th International Conference on Electronics and Communication Engineering (ICECE), 2021, pp. 286–290.
[20]M. Zhao, Y. Shu, S. Liu and G. Xu, “Electricity price forecast using meteorology data: A study in australian energy market, ” in 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), 2019, pp. 1–2.
[21]S. Sahoo, S. C. Swain and R. Dash, “An analysis of machine learning methods for electricity price forecasting, ” in 1st International Conference on Circuits, Power and Intelligent Systems (CCPIS), 2023, pp. 1–5.
[22]T. Bao, X. Wang, N. Mahdavi, C. McCarthy and D. Rezazadegan, “Dynamic xgboost-based quantile predictor for real-time electricity price forecasting, ” in IEEE International Conference on Energy Technologies for Future Grids (ETFG), 2023, pp. 1–6.
[23]J. Han, J. Pei and H. Tong, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, Oct. 2022.
[24]Scikit-Learn, “Sklearn. preprocessing. Standard Scaler, ” https://scikit-learn.org/ stable/modules/generated/sklearn.preprocessing.StandardScaler.html, last accessed on june 2024.
[25]S. Raschka, “Model evaluation, model selection, and algorithm selection in machine learning,” arXiv preprint, arXiv:1811.12808, 2018, pp. 1-49, last accessed on July 2023.
[26]P. L. Bartlett et al., “Model selection and error estimation,” Machine Learning journal, Springer, vol. 48, pp. 85–113, 2002.
[27]P. Schratz et al., “Hyperparameter tuning and performance assessment of statistical and machinelearning algorithms using spatial data,” Ecological Modelling, vol. 406, pp. 109– 120, 2019.
[28]L. Yang et al., “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295– 316, 2020.
[29]C. Gena et al., “An affective and adaptive educational robot,” arXiv preprint, arXiv:2205.10222, 2022, pp. 1– 11.
[30]Fast API authority, “Fastapi,” https://fastapi.tiangolo.com/, last accessed on June 2024.
[31]M. P. Inc, “React Native,” https://reactnative.dev/, last accessed on May 2024.