Research On Improved ABC-RBF Short-term Electric Load Forecasting Considering Demand Response

In the process of the continuous development of the power industry and the continuous improvement of the intelligent level of the power grid,users have put forward higher requirements for electrical energy,and the importance of power load forecasting has become increasingly prominent.High-precision power load forecasting is used to be a prerequisite for ensuring the safe and stable operation of the power grid.Traditional load forecasting methods,such as regression analysis,have low accuracy and poor real-time forecasting.This article aims to improve the accuracy of short-term power load forecasting.Aiming at the existing problems,based on the theoretical foundation at home and abroad and the existing research results,and improve it.Combine the existing data to carry out the experimental simulation of short-term load forecasting.The following contents were studied.First,the research status of various power load forecasting methods,radial basis function neural network and demand response is described,and the current short-term load forecasting problems are summarized.Secondly,combined with the characteristics of power load,traditional power load classification,and the influence of temperature,weather,and day type on power load,the original data is processed horizontally and vertically with missing data and abnormal data.Subsequently,a radial basis neural network short-term power load forecasting model considering the comprehensive influence factors of demand response is constructed.The semi-trapezoidal membership function is used to solve the problem of fuzzy user response.Then the quantified results of demand response accuracy are introduced into the radial basis function neural network model.When considering the demand-side response,the RBF model has a good performance in load forecasting.Aiming at the problem of setting the initial parameters of the radial basis function neural network,the artificial bee colony algorithm(ABC algorithm)is further used to optimize the model.The optimal solution generated by the ABC algorithm is used as the initial parameter of the radial basis function neural network,and the ABC-RBF model is established by combining the two algorithms.Through the research of fuzzy theory,find out the combination of fuzzy theory and artificial neural network.The input information of the ABC-RBF neural network is classified by the membership function to improve the classification accuracy of load forecasting.The optimized fuzzy ABC-RBF neural network model is applied to the load forecasting example after considering the influence factors of demand response,and then it is compared and analyzed with the classic RBF neural network algorithm.Through the research of the above content,this paper fully combines radial basis neural network,artificial bee colony algorithm and fuzzy theory for the problem of short-term power load forecasting.The experimental results show that,considering the demand side response,compared with the traditional RBF neural network algorithm,the fuzzy ABC-RBF neural network not only has higher prediction accuracy,but also converges more efficiently to the smallest error in the model training process.value.It provides a new direction for short-term load forecasting,and also lays a foundation for establishing the relationship between load forecasting and electricity price forecasting.