Hierarchical Clustering and Deep Learning for Short-Term Load Forecasting with Influenced Factors
Stable and reliable electricity is one of the essential things that must be maintained by the transmission system operator (TSO). That can be achieved when the TSO is able to set the balance between demand and production. To maintain the balance between production and demand, TSO should estimate how much demand must be served. In order to do that, the next day short-term load forecasting is an essential step that TSO should be done. Generally, load forecasting can be done through conventional techniques such as least square, time series, etc. However, this method has been sought over time as the electricity demand is increasing significantly over the years. Hence, this paper proposed another approach for short-term load forecasting using Deep Neural Networks, widely known as Long Short-Term Memory (LSTM). In addition, this paper clusters historical electrical loads to obtain similar patterns into several clusters before forecasting. We also explored other influence factors in the observed days, such as weather conditions and the human activity cycle represented by holidays, in a neural network-based classification model to predict the targeted clusters of electrical loads. East Java sub-system is used as the test system to investigate the efficacy of the proposed load forecasting method. From the simulation results, it is found that the proposed method could provide a better forecast on all indicators compared to the conventional method, as indicated by MaxAPE and MAPE are around 4,91% and 2,02%, while the RMSE is 112,08 MW.
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