基于天气信息的短期冷热电负荷联合预测方法

Short-term Cooling and Heating Power Load Prediction Method Based on Multi-weather Information

随着可再生能源大量接入,增加了能源互联网的波动性与多样性,对冷热电负荷预测的精确度和稳定性提出了更高的要求,冷热电负荷的精准预测是能源互联网运行优化的重要前提,并对需求侧分析具有重要意义。利用天气信息,提出了一种基于气象信息的短期冷热电负荷联合预测方法。该方法包括区域天气预测与冷热电负荷联合预测两大步骤。在区域天气预测中,首先充分利用历史天气、实测天气与天气预报信息,采用调整误差法,对一指定区域进行天气预测;之后利用历史负荷数据、历史天气数据与区域天气预测数据,采取遗传算法优化BP神经网络(genetic algorithm to optimize BPneuralnetwork,GA-BP)预测算法,对冷热电负荷进行联合预测。仿真结果表明该方法能够有效提高负荷预测精度。

The access of a large amount of renewable energypromotes the fluctuation and diversity of the energy Internet and puts forward the higher requirements for the accuracy and stability of the cooling and heating power load forecasting,which is an important prerequisite for the operation optimization of the energy Internet and of great significance for the demand side analysis.Based on the weather information,a combined forecasting method of short-term cooling and heating power load is proposed.This method includes two steps:the regional weather forecast and the combined forecast of cooling and heating power load.In the regional weather forecasting,first of all,this method makes full use of the historical weather information,the measured weather situations and the weather forecasts to forecast the weather in a designated area with the adjustment error method.Then this method uses the historical load data,the historical weather data and the weather forecast data,and adopts the Genetic Algorithm to optimize BP Neural Network(GA-BP)prediction algorithm to jointly predict the cooling and heating power load.The simulation results show that this method can effectively improve the accuracy of the cooling and heating power load prediction.