Study on short-term load forecasting method considering meteorological factors of offshore oilfield group microgrid

Accuracy is an important parameter to measure the performance of load prediction. As the power load of offshore oilfield group microgrid is greatly affected by meteorological factors such as temperature, wind speed and so on, the short-term load prediction results are greatly deviated from the actual load. The influence of meteorological factors is considered in this paper and the exploitation suitability index is put forward to comprehensively quantify meteorological factors. Dragonfly Algorithm-based support vector machine (DA-SVM) algorithm and exploitation suitability index (ESI) are used to predict the short-term load of a certain offshore oilfield group microgrid in Bohai sea. The experimental result shows that the accuracy of the prediction results obtained by using the exploitation suitability index to quantify meteorological factors is improved.