Title

Short-Term Electricity Demand Forecasting Based on Multiple LSTMs

RIS ID

135748

Publication Details

Yong, B., Shen, Z., Wei, Y., Shen, J. & Zhou, Q. (2020). Short-Term Electricity Demand Forecasting Based on Multiple LSTMs. Lecture Notes in Computer Science, 11691 192-200. Guangzhou, China The 10th International Conference on Brain-Inspired Cognitive Systems BICS 2019

Abstract

In recent years, the problem of unbalanced demand and supply in electricity power industry has seriously affected the development of smart grid, especially in the capacity planning, power dispatching and electric power system control. Electricity demand forecasting, as a key solution to the problem, has been widely studied. However, electricity demand is influenced by many factors and nonlinear dependencies, which makes it difficult to forecast accurately. On the other hand, deep neural network technologies are developing rapidly and have been tried in time series forecasting problems. Hence, this paper proposes a novel deep learning model, which is based on the multiple Long Short-Term Memory (LSTM) neural networks to solve the problem of short-term electricity demand forecasting. Compared with autoregressive integrated moving average model (ARIMA) and back propagation neural network (BPNN), our model demonstrates competitive forecast accuracy, which proves that our model is promising for electricity demand forecasting.

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/978-3-030-39431-8_18