Air-conditioning load forecasting based on seasonal decomposition and ARIMA model

Influenced by the ever-increasing electricity consumption and ambient temperature, the proportion of the air-conditioning load reaches up to 50% in China and shows a trend of further growth, which gives rise to the concerns of formulating effective measures to control the load growth and improve the load profile. The existing air-conditioning load forecasting methods are mostly for the total amount in a given area, rather than for a specific user or a block of users, and do not distinguish the users’ specific types, and hence lead to less accurate prediction results. Given this background, a load forecasting method for point loads is developed based on seasonal decomposition and the AutoRegressive Integrated Moving Average (ARIMA) model. Specifically, a correction method for seasonal component based on the maximum air-conditioning load is presented, and the complete annual air-conditioning load curve can then be obtained. In this way, the accuracy of load forecasting is effectively improved. This forecasting method can be widely used in applications such as the planning of distribution systems, microgrids, and virtual power plants, and can also provide an effective decision-making basis for formulating actual demand-side response strategies and developing control measures to regulate the air-conditioning load, and hence to reduce the peak load.