Machine Learning-Based Analysis and Forecasting of Electricity Demand in Misamis Occidental, Philippines
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65392-6_8
Reference27 articles.
1. Santos, A.G.: Forecasting residential electricity demand in the Philippines using an error correction model. Philipp. Rev. Econ. 57(1) (2021). (Online ISSN 2984-8156)
2. Albuquerque, P.C., Cajueiro, D.O., Rossi, M.D.: Machine learning models for forecasting power electricity consumption using a high dimensional dataset. Expert Syst. Appl. 187 (2022)
3. Geetha, R., Ramyadevi, K., Balasubramanian, M.: Prediction of domestic power peak demand and consumption using supervised machine learning with smart meter dataset. Multimed. Tools Appl. 80(13), 19675–19693 (2021)
4. He, Z., Zhao, C., Huang, Y.: Multivariate time series deep spatiotemporal forecasting with graph neural network. Appl. Sci. 12(11), 5731 (2022)
5. Lee, M.H.L., et al.: A comparative study of forecasting electricity consumption using machine learning models. Mathematics 10(8), 1329 (2022)
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