The Elman Network of Heat Load Forecast Based on the Temperature and Sunlight Factor

In urban district heating systems, the change of heat load is greatly influenced by various exterior factors. In order to meet the demand of heating system while achieving energy conservation and environmental protection, it is in this study, many kinds of artificial neural networks are compared, and a kind of Elman neural network is proposed for modeling heat load forecasting based on the temperature and the sunlight factor. The method obtains the real-time weather temperature from the Application Programming Interface (API) interface of the meteorological web site, added the illumination intensity as an input of the heat load forecasting model, and established the sample data sequence of the forecasting model. The real-time data is used to update history data and it makes up the new inputs to achieve short-term heat load rolling forecasts. The simulation results show that this method can accurately predict the future heat load, and achieve the purpose of on-demand heating, energy conservation, and environmental protection.