基于自适应卡尔曼滤波在气象影响下负荷预测

如今电网系统中所构成电力负荷的电器越来越多，其中像空调等受气象影响的负荷所占比例持续升高，那么气象因素（温度、湿度、降雨量等）对电网的影响自然越来越突出，因此短期负荷预测将气象因素考虑进去，能够大大提升预测精度。根据某地区六年的电力负荷数据，构建卡尔曼滤波模型，可以给出高效准确的预测结果。然后将气象因素考虑到自适应卡尔曼滤波模型，通过不断对状态估计进行修正，得到计及气象因素影响的负荷预测结果精度更高。通过MATLAB 仿真，说明这种算法比较传统的卡尔曼滤波具有更高的预测精度，而且这种改进后的算法对实现短期负荷预测提供了一条新的途径。

Short-term load forecasting under meteorological influence based on the adaptive Kalman filter

Nowadays, there are more and more electric appliances constituting power load in the power grid system. Among them, the proportion of load affected by weather, such as air conditioning, keeps increasing. Therefore, the influence of meteorological factors (temperature, humidity, rainfall, etc.) on the power grid is more and more prominent. Considering meteorological factors becomes one of the main means for the dispatching center to further improve the load forecasting accuracy. According to the load data of a certain area for six years, the Kalman filter model can give the accurate and efficient prediction results. Then, the meteorological factors are taken into account in the adaptive Kalman filter model, and the load prediction results with meteorological factors taken into account are more accurate through constant modification of the state estimation. Through the MATLAB simulation, it shows that the algorithm is more accurate than traditional Kalman filter, and the modified algorithm provides a new way for short-term load forecasting.