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Title	Comparative Lstm And Svm Machine Learning Approaches For Energy Consumption Prediction: Case Study In Akmola
ID_Doc	15041
Authors	Satan A.; Khamzina A.; Toktarbayev D.; Sotsial Z.; Bapiyev I.; Zhakiyev N.
Year	2022
Published	SIST 2022 - 2022 International Conference on Smart Information Systems and Technologies, Proceedings
DOI	http://dx.doi.org/10.1109/SIST54437.2022.9945776
Abstract	Load forecasting is one of the most important aspects of power systems and Smart City concept. Forecasting a load of a power system with great accuracy not only for a few weeks but also for several years is important in terms of both technical and economic aspects. Nowadays, there exist many methods that are modeled to forecast the consumer's load. This paper will mainly focus on developing forecasting model basing on two Machine Learning (ML) techniques. The first is the branch of RNN method, which is named as Long-Short Term Memory (LSTM). The second one is Support Vector Machine. Both methods solve the problems related with prediction and show high accuracy and precision in time-series application. © 2022 IEEE.
Author Keywords	Energy Consumption Prediction; Energy System; LSTM; Machine Learning; SVM

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