Research On Micro-Grid Photovoltaic Power Generation And Load Short-term Forecasting Based On Neural Network

Since the volatility and randomness of the photovoltaic power generation and the power load of the micro-grid will have a great impact on the stable operation of the micro-grid system,the energy exchange between the power generation unit and the load in the micro-grid becomes extremely complicated.In order to ensure the safe and stable operation of the micro-grid and the normal production and life of the users,accurate and reliable short-term prediction of photovoltaic power and load has im portant practical significance.In the short-term prediction of micro-grid photovoltaic power generation,this paper designs a short-term prediction model of Long-Short Term Memory(LSTM)neural network based on similar day optimization.Firstly,the micro-grid photovoltaic power generation characteristics are analyzed effectively and their influencing factors are fully demonstrated.Secondly,in order to improve the prediction accuracy of photovoltaic power generation under different weather types(fine,cloudy and rainy days),a similar daily screening model based on K-Means clustering algorithm is designed.Finally,a short-term prediction model of micro-grid photovoltaic power generation based on LSTM neural network was designed and implemented.The multi-scale and high-resolution short-term prediction of photovoltaic power generation in different weather types in the next 24,48 and 72 hours was designed.In the short-term prediction of micro-grid load,this paper designs a short-term prediction model of LSTM neural network load based on similar type of daily optimization.Firstly,the typical daily and weekly load curve characteristics of the micro-grid are analyzed,the meteorological factors and day types affecting the power load are quantified.Secondly,a similar type daily screening model is designed,and the initial data set is divided into a working day training set and a rest day training set.Finally,the short-term prediction model of micro-grid load based on LSTM neural network is designed and implemented.and the power load of working days and rest days in the next 24,48,and 72 hours is comprehensively predicted.Through the comparative analysis of the above-mentioned prediction results through the evaluation indicators stipulated by the national standards,the prediction results of the prediction methods described in this paper meet the requirements of the national standard.The Mean Absolute Error(MAE)of the short-term prediction results of photovoltaic power generation reached 7.14%,and the Mean Absolute Percentage Error(MAPE)of the short-term load forecast reached 1.75%.Compared with the Back Propagation(BP)neural network algorithm commonly used in the research field,the algorithm is improved by 6.45%and 1.12%,respectively.The prediction method described in this paper has important application value for micro-grid photovoltaic power generation and short-term load forecasting.