Abstract
As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for residential electricity consumption, have become common problems across the country. Accurate residential load forecasting can provide strong data support for the operation of electricity demand response and the incentive setting of the response. For the accuracy and stability of residential electricity load forecasting, a forecasting model is presented in this paper based on fuzzy cluster analysis (FC), least-squares support vector machine (LSSVM), and a fireworks algorithm (FWA). First of all, to reduce the redundancy of input data, it is necessary to reduce the dimension of data features. Then, FWA is used to optimize the arguments γ and σ2 of LSSVM, where γ is the penalty factor and σ2 denotes the kernel width. Finally, a load forecasting method of FC–FWA–LSSVM is developed. Relevant data from Beijing, China, are selected for training tests to demonstrate the effectiveness of the proposed model. The results show that the FC–FWA–LSSVM hybrid model proposed in this paper has high accuracy in residential power load forecasting, and the model has good stability and versatility.
Highlights
Accepted: 20 January 2022With a rapidly growing economy, the massive consumption of non-renewable energy, the deterioration of people’s living environment, and the energy crisis, how to improve energy utilization and achieve the coordinated development of economy and energy have become the focus of attention for countries around the globe
The CS and bat algorithms cannot converge to the optimal point, so they are susceptible to slip into local optimization, which leads to the reduction in load forecasting accuracy
This paper proposes to use the fireworks algorithm to optimize the arguments of leastsquares support vector machine (LSSVM)
Summary
With a rapidly growing economy, the massive consumption of non-renewable energy, the deterioration of people’s living environment, and the energy crisis, how to improve energy utilization and achieve the coordinated development of economy and energy have become the focus of attention for countries around the globe. Scholars have gradually applied intelligent algorithms to the field of load prediction Since intelligent algorithms such as artificial neural networks can simulate the human brain mechanism, the prediction accuracy has been improved with the help of their self-learning and self-seeking functions to simulate the changing pattern of the predicted object and build a suitable model [7]. The CS and bat algorithms cannot converge to the optimal point, so they are susceptible to slip into local optimization, which leads to the reduction in load forecasting accuracy For this reason, this paper proposes to use the fireworks algorithm to optimize the arguments of LSSVM. This paper analyzes the influencing factors of residential electricity load and constructs a residential electricity load forecasting model (FC–FWA–LSSVM) based on fuzzy cluster analysis and the fireworks algorithm to optimize LSSVM.
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