IEEE Access (Jan 2023)
A Hybrid Model for Short-Term Load Forecasting Based on Novel Input Sequence Selection and CSO Optimized Depth Belief Network
Abstract
Accurate power load forecasting is crucial to the safe and stable operation of power systems. In the context of spot market, the dynamically changing real-time market tariff gives the “commodity” property of electricity and changes the electricity consumption behavior of customers and the electricity consumption at each time, which significantly aggravates the difficulty of electricity load forecasting. To address the problems of many influencing factors, difficult input sequence selection and insufficient feature extraction capability of the prediction model, we propose a hybrid prediction model that combines novel input sequence selection and longitudinal crossover algorithm (CSO) to optimize deep belief networks. Firstly, the real-time electricity price is incorporated as an influencing factor into the prediction model input, which improves the prediction model influencing factor system in the market environment; secondly, the similarity between the historical load sequence influencing factor and the sequence of load influencing factors of the day to be predicted is considered from two perspectives of distance and trend, and the historical load sequence is reasonably selected using the comprehensive similarity, and then the prediction model input sequence is determined; finally, the deep belief Finally, the load forecasting model is constructed by using deep belief network, and the key parameters such as threshold value of the forecasting model are optimized by CSO algorithm to achieve accurate power load forecasting. The proposed hybrid forecasting model is validated by comparing and analyzing the forecasting results of different input sequences and different methods with the simulation of Singapore electricity market data. In addition, input series of different time scales are set to find the best sample set to avoid the reduction of forecasting accuracy caused by data redundancy in the sample set, and a sample set of 6 months is appropriate for load forecasting by the method in this paper.
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