A short-term load prediction method considering somatosensory temperature and radiation intensity

The invention discloses a short-term load prediction method considering somatosensory temperature and radiation intensity. The short-term load prediction method comprises the following steps of 1) inquiring sample data such as historical load and weather; 2) calculating historical somatosensory temperature data and a daily load level; 3) selecting an optimal'mode similar day 'from the historical sample data set on the basis of the day type information and meteorological data of the to-be-predicted day, and finally calculating to obtain a normalized load curve; 4) establishing a neural networkprediction model considering the somatosensory temperature and the sunlight intensity to obtain the load level of the to-be-predicted day; and 5) calculating load data of the to-be-predicted day through the normalization curve and the load level. According to the method, the influence on the somatosensory temperature of the load and the sunlight intensity of the distributed photovoltaic power generation are fully considered, the change rule of the historical load is fully considered, the load level and the load mode are separately predicted, the input dimension of the neural network is reduced, the network training load is reduced, and the calculation efficiency is improved.