Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION
Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions. This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.
Abdel-Aal, R. E., & Al-Garni, A. Z. (1997). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy, 22(11), 1059–1069. https://doi.org/10.1016/S0360-5442(97)00032-7
Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2019). Ensemble of relevance vector machines and boosted trees for electricity price forecasting. Applied Energy, 250(May), 540–548. https://doi.org/10.1016/j.apenergy.2019.05.062
Ashfaque, J. M. (2020). Electricity in France. https://www.kaggle.com/ukveteran/electricity-france/metadata
Brockmann, D., Hufnagel, L., & Geisel, T. (2006). Data Mining and Knowledge Discovery Handbook. In Springer. https://doi.org/10.1038/nature04292
Choi, H., Ryu, S., & Kim, H. (2018). Short-Term Load Forecasting based on ResNet and LSTM. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2018. https://doi.org/10.1109/SmartGridComm.2018.8587554
Dodamani, S. N., Shetty, V. J., & Magadum, R. B. (2015). Short term load forecast based on time series analysis: A case study. Proceedings of IEEE International Conference on Technological Advancements in Power and Energy, TAP Energy 2015, 299–303. https://doi.org/10.1109/TAPENERGY.2015.7229635
Fang, T., & Lahdelma, R. (2016). Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system. Applied Energy, 179, 544–552. https://doi.org/10.1016/j.apenergy.2016.06.133
Ferreira, V. H., & Alves da Silva, A. P. (2007). Toward estimating autonomous neural network-based electric load forecasters. IEEE Transactions on Power Systems, 22(4), 1554–1562. https://doi.org/10.1109/TPWRS.2007.908438
Fumo, N., & Rafe Biswas, M. A. (2015). Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews, 47, 332–343. https://doi.org/10.1016/j.rser.2015.03.035
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. In Data Mining. https://doi.org/10.1016/b978-0-12-381479-1.00001-0
He, Y., Qin, Y., Wang, S., Wang, X., & Wang, C. (2019). Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network. Applied Energy, 233–234(May 2018), 565–575. https://doi.org/10.1016/j.apenergy.2018.10.061
Islyaev, S., & Date, P. (2015). Electricity futures price models: Calibration and forecasting. European Journal of Operational Research, 247(1), 144–154. https://doi.org/10.1016/j.ejor.2015.05.063
Kaytez, F. (2020). A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption. Energy, 197. https://doi.org/10.1016/j.energy.2020.117200
Kim, T. Y., & Cho, S. B. (2019). Predicting residential energy consumption using CNN-LSTM neural networks. Energy, 182, 72–81. https://doi.org/10.1016/j.energy.2019.05.230
Lee, C. M., & Ko, C. N. (2009). Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm. Neurocomputing, 73(1–3), 449–460. https://doi.org/10.1016/j.neucom.2009.07.005
Nadtoka, I. I., & Al-Zihery Balasim, M. (2015). Mathematical modeling and short-term forecasting of electricity consumption of the power system, with due account of air temperature and natural illumination, based on support vector machine and particle swarm. Procedia Engineering, 129, 657–663. https://doi.org/10.1016/j.proeng.2015.12.087
Nyberg, H., & Saikkonen, P. (2014). Forecasting with a noncausal VAR model. Computational Statistics and Data Analysis, 76, 536–555. https://doi.org/10.1016/j.csda.2013.10.014
Pombeiro, H., Santos, R., Carreira, P., Silva, C., & Sousa, J. M. C. (2017). Comparative assessment of low-complexity models to predict electricity consumption in an institutional building: Linear regression vs. fuzzy modeling vs. neural networks. Energy and Buildings, 146, 141–151. https://doi.org/10.1016/j.enbuild.2017.04.032
Sadownik, R., & Barbosa, E. P. (1999). Short‐term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting, 18(3), 215–224. https://doi.org/10.1002/(sici)1099-131x(199905)18:3<215::aid-for719>3.3.co;2-2
Satre-Meloy, A. (2019). Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models. Energy, 174, 148–168. https://doi.org/10.1016/j.energy.2019.01.157
Setiyorini, T. (2020). Laporan Akhir Penelitian Mandiri.
Shao, M., Wang, X., Bu, Z., Chen, X., & Wang, Y. (2020). Prediction of energy consumption in hotel buildings via support vector machines. Sustainable Cities and Society, 57(March), 102128. https://doi.org/10.1016/j.scs.2020.102128
Tan, Z., De, G., Li, M., Lin, H., Yang, S., Huang, L., & Tan, Q. (2020). Combined electricity-heat-cooling-gas load forecasting model for an integrated energy system based on multi-task learning and least square support vector machine. Journal of Cleaner Production, 248. https://doi.org/10.1016/j.jclepro.2019.119252
Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139–152. https://doi.org/10.1016/j.ejor.2009.10.003
Yan, K., Li, W., Ji, Z., Qi, M., & Du, Y. (2019). A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households. IEEE Access, 7, 157633–157642. https://doi.org/10.1109/ACCESS.2019.2949065
Yang, W., Wang, J., Niu, T., & Du, P. (2020). A novel system for multi-step electricity price forecasting for electricity market management. Applied Soft Computing Journal, 88, 106029. https://doi.org/10.1016/j.asoc.2019.106029
Zagrebina, S. A., Mokhov, V. G., & Tsimbol, V. I. (2019). Electrical energy consumption prediction is based on the recurrent neural network. Procedia Computer Science, 150, 340–346. https://doi.org/10.1016/j.procs.2019.02.061
Zahid, A. (2020). Smart Meter Energy(kW) Demand Forecasting. Kaggle.com. https://www.kaggle.com/asimzahid/smart-meter-energykw-demand-forecasting
Zhao, G. Y., Liu, Z. Y., He, Y., Cao, H. J., & Guo, Y. B. (2017). Energy consumption in machining: Classification, prediction, and reduction strategy. In Energy (Vol. 133, pp. 142–157). https://doi.org/10.1016/j.energy.2017.05.110
Zheng, Z., Chen, H., & Luo, X. (2019). Spatial granularity analysis on electricity consumption prediction using LSTM recurrent neural network. Energy Procedia, 158, 2713–2718. https://doi.org/10.1016/j.egypro.2019.02.027
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