Abstract
Load forecasting is a technique used by power companies to predict the energy needed to balance the supply and load demand at all times. It is also a mandatory requirement for proper functioning of the electrical power supply industry. Load forecasting is critical to provide decision-making support for power generation. An accurate load forecasting can ensure sufficient power being generated to fulfill actual need of the community and reduce waste of generation. In this paper, a regression-based method is presented for load forecasting in Meghalaya. The efficiency of the methodology is evaluated on the dataset and the predicted values are compared with the actual maximum load demands. The method is found to be effective in accurately forecasting of maximum load demand during the winter season in Meghalaya. This method can be applied to any place in the world having colder climate, with load dependency on heating elements, especially during the winter season.
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References
Ali D, Yohanna M, Puwu MI, Garkida BM (2016) Long-term load forecast modelling using a fuzzy logic approach. Pacific Sci Rev: Nat Sci Eng 18(2):123–127
Amral N, Ozveren CS, King D (2007) Short term load forecasting using multiple linear regression. In: 42nd international universities power engineering conference proceedings. IEEE, Brighton, UK, pp 1192–1198
Chen K, Chen K, Wang Q, He Z, Hu J, He J (2018) Short-term load forecasting with deep residual networks. IEEE Trans Smart Grid 10(4):3943–3952
Dudek G (2016) Pattern-based local linear regression models for short-term load forecasting. Electric Power Syst Res 130:139–147
Dudek G (2020) Multilayer perceptron for short-term load forecasting: from global to local approach. Neural Comput Appl 32(8):3695–3707
Guo W, Che L, Shahidehpour M, Wan X (2021) Machine-learning based methods in short-term load forecasting. Electr J 34(1):106884
Hamlich M, Eddine Belbounaguia N (2019) Short-term load forecasting using machine learning and periodicity decomposition. AIMS Energy 7(3):382–394
He W (2017) Load forecasting via deep neural networks. Procedia Comput Sci 122:308–314
Islam BU (2011) Comparison of conventional and modern load forecasting techniques based on artificial intelligence and expert systems. Int J Comput Sci Issues (IJCSI) 8(5):504
Nassif AB, Soudan B, Azzeh M, Attilli I, AlMulla O (2021) Artificial intelligence and statistical techniques in short-term load forecasting: a review. arXiv preprint arXiv:2201.00437
Rana M, Koprinska I (2016) Forecasting electricity load with advanced wavelet neural networks. Neurocomputing 182:118–132
Reddy MD, Valley RK, Vishali N (2017) Load forecasting using linear regression analysis in time series model for RGUKT, R.K. Valley Campus HT Feeder. Int J Eng Res Technol (IJERT) 6(5):1–5
Sharma S, Chouzenoux É, Elvira V, Majumdar A (2022) A dynamical model for stock forecasting via deep recurrent dictionary learning. HAL Archives. HAL Preprint https://hal.archives-ouvertes.fr/hal-03654152/file/Final_version_Deep_Recurrent_Dictionary_Learning_NoColor.pdf
Singh AK, Ibraheem SK, Muazzam M, Chaturvedi DK (2013) An overview of electricity demand forecasting techniques. Network Complex Syst 3(3):38–48
Smyl S, Dudek G, Pełka P (2021) ES-dRNN: a hybrid exponential smoothing and dilated recurrent neural network model for short-term load forecasting. arXiv preprint arXiv:2112.02663
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Mawtyllup, B., Goswami, B. (2024). Development of Machine Learning Based Daily Peak Load Forecasting System for Winter Season in the State of Meghalaya in India. In: Deka, J.K., Robi, P.S., Sharma, B. (eds) Emerging Technology for Sustainable Development. EGTET 2022. Lecture Notes in Electrical Engineering, vol 1061. Springer, Singapore. https://doi.org/10.1007/978-981-99-4362-3_23
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DOI: https://doi.org/10.1007/978-981-99-4362-3_23
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