[2D6-GS-2] Machine learning: applications (2)

[2D6-GS-2-05] A Comparative Study of Electricity Demand Forecasting Using Recurrent Neural Network

〇Masaya Nakayama¹, Shoichi Urano¹ (1. Meiji University)

Keywords:Recurrent Neural Network, electricity demand forecasting, RNN, LSTM, Machine Leraning

Electricity is difficult to store, so it is important to maintain a balance between supply and demand. There are conventional machine learning methods that use RNN, which can learn short-term dependencies, but have difficulty learning long-term dependencies. LSTM, a kind of derivative of RNN, is proposed as one of the solutions to this problem. In this paper, we aim to improve the accuracy of electricity demand forecasting by comparing and examining the effects of these two methods on electricity demand forecasting.