Deep Learning for Daily Peak Load Forecasting–A Novel Gated Recurrent Neural Network Combining Dynamic Time Warping

Abstract

Daily peak load forecasting is an essential tool for decision making in power system operation and planning. However, the daily peak load is a nonlinear, nonstationary, and volatile time series, which makes it difficult to be forecasted accurately. This paper, for the first time, proposes a bespoke gated recurrent neural network combining dynamic time warping (DTW) for accurate daily peak load forecasting. The shape-based DTW distance is used to match the most similar load curve, which can capture trends in load changes. By analyzing the relationship between the load curve and the cycle of human social activities, the some-hot encoding scheme is first applied on the discrete variables to expand the features to further characterize their impact on load curves. Then, a three-layer gated recurrent neural network is developed to forecast daily peak load. The proposed algorithm is implemented on the Theano deep learning platform and tested on the loaded dataset of the European Network on Intelligent Technologies. The simulation results show that the proposed algorithm achieves satisfactory results compared with other algorithms using the same dataset in this paper.

Keywords

• Daily peak load forecasting
• dynamic time warping
• one-hot encoding
• gated recurrent unit