Research On Power Load Forecasting Based On Improved Neural Network

With the continuous deepening of power market reforms,accurate short-term power load forecasting not only guarantees the security and stability of the power grid and economic operation,but also the basis for arranging power generation and power dispatching in a market environment.Due to the randomness and periodicity of power load sequences and the complexity of influencing factors,the prediction accuracy of many models has not yet reached the level of satisfaction required by the power grid.In the future,more security,economic,and stability of power grid operations will be proposed.With high requirements,the development of modern power systems must improve the quality of power system load forecasting.Therefore,it is necessary to explore a more simple,efficient,and accurate short-term power load forecasting method.To improve the accuracy of short-term load forecasting of power system,according to the nonlinearity and uncertainty of short-term load sequence,a short-term power load forecasting method combined with wavelet neural network(WNN)and adaptive mutation bat optimization algorithm(AMBA)which is based on the variance of the population's fitness is proposed.The model determines the mutation probability of the current optimal individual based on the variance of the population's fitness and the current optimal solution,and performs the Gaussian mutation on the global optimal individual,and performs the second optimization on the bat individuals after mutation.Then AMBA is employed to optimize the network parameters of wavelet neural network,improving the prediction accuracy of wavelet neural network and speeding up its training,then the AMBA-WNN forecasting model is built.The AMBA-WNN model is used to predict short-term load of a certain city in China,by analysis of case study,the results show that the model can effectively improve the accuracy of short-term load forecasting and has a good practical significance.As China's economy enters the new normal,various industries and environmental protection policies are further promoted,and the power load shows a trend of slowing down and fluctuating characteristics.The change in load growth mode has increased the difficulty of medium and long-term load forecasting.The effects have also changed to varying degrees.The reason for this phenomenon is,in the final analysis,that the policy changes in the economic,industrial,and environmental protection aspects have profoundly affected the power load.However,the policy has strong uncertainty,many factors affect it,and the relationship between them and their impact on the power load are complicated.Therefore,comprehensively and systematically sorting out the relevantfactors affecting power load under policy factors,analyzing its influence mechanism on power load,and establishing a correlation model to predict it are of great significance for guiding power planning and load forecasting in the new situation.At present,China's power load development is facing new economic conditions,industrial restructuring,and energy conservation and emission reduction policies.In this context,in order to improve the accuracy of load forecasting under the influence of policy factors,and to solve the problems of ambiguous policy factors,difficult quantification,and difficult to integrate into the load model,a medium-and long-term load forecasting model considering policy factors is proposed.Firstly,by analyzing the impact of various policies on the power load,a macro-micro-level and hierarchical policy impact factor index system was constructed,which systematically reflected the impact of the policy on the load;then,the traditional gray correlation analysis model In the future,the power development situation lacks sufficient considerations.Through the principal component analysis,the weighted average of the factor indicators is proposed.An improved gray correlation analysis model is proposed to realize the empowerment of various policy indicators on the power load.It combines subjective and objective empowerment.Finally,the fuzzy cluster analysis method is used to predict the load under the influence of policy factors.The proposed model can better solve the difficulties brought by load fluctuations to medium and long-term load forecasting,and is applicable to medium and long-term load forecasting in the context of policy changes.The analysis of examples shows that the proposed method has better prediction accuracy and engineering application value than the conventional prediction methods such as time series extrapolation and elastic coefficient.