Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

Abstract

In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.

Improved GWO and its application in parameter optimization of Elman neural network

“…SI includes mothflame optimization algorithm (MFO) [32,33], white shark optimizer (WSO) [34], whale optimization algorithm (WOA) [17], sparrow search optimization algorithm (SSA) [35], and others. In SI, the particle swarm optimization (PSO) [13] is the most popular algorithm, which updates the location of birds to find the most food.…”

“…Therefore, researchers have used metaheuristic algorithms in Elman as an optimization strategy for network structures, and a series of more meaningful results have been achieved so far. For example, Zhang et al used an improved arithmetic optimizer (IAO) to train the Elman network structure [11]; For the soil salinity prediction problem, the sine cosine algorithm (SCA) was applied to adjusting the parameters of Elman [12], and the experimental results demonstrated that SCA could improve the prediction efficiency of Elman; Some researchers used the particle swarm optimization (PSO) algorithm to optimize Elman parameters and PSO-Elman based on load prediction model [13], compaction density evaluation model [14] and parameter evaluation model were constructed [15]; Metaheuristic algorithms were combined for adjusting the weights and thresholds of Elman. For example, the ant colony algorithm (ACO) and genetic algorithm (GA) were combined to form AGA-Elman [16]; SUN et al developed an Elman prediction model based on a whale optimization algorithm (WOA) [17].…”

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

“…is simulation helps researchers circumvent the issue of having to charge electric vehicles at fixed locations at the same time and more accurately reflects the stochastic nature of the spatial movement of electric vehicles in real time. Other researchers categorize the travel space according to the purpose of the activity, and then they use the travel chain and the Markov primary state transfer matrix to obtain the characteristics of the vehicle's spatial movement [19].…”

Hourly Heat Load Prediction for Residential Buildings Based on Multiple Combination Models: A Comparative Study

“…Statistical methods tend to overfit when handling diverse influencing factors, leading to poor predictive performance of the models [4][5][6][7]. With the wide application of artificial intelligence algorithms in the prediction field [8][9][10][11], more and more scholars have started researching load-predicting methods founded on machine learning [12][13][14]. Some commonly used approaches in machine learning include artificial neural networks (ANN) [15], support vector machines [16], extreme learning machines [17], random forest (RF) [18], and regression trees [19].…”