An innovative integrated model using the singular spectrum analysis and nonlinear multi-layer perceptron network optimized by hybrid intelligent algorithm for short-term load forecasting

• A new integrated model is developed for power load forecasting.
• The model is based on the LDS, SSA, APSOGSA and the NMLPNN.
• A hybrid intelligent optimization algorithm is employed to determine the parameters of NMLPNN.
• The proposed integrated method indicates the superiority and promising performance.

Short-term power load forecasting is receiving increasing attention, especially because of intrinsic difficulties and practical applications. In this paper, the novel integrated approaches, combining longitudinal data selection (LDS), singular spectrum analysis (SSA) technique, adaptive particle swarm optimization based on gravitational search algorithm (APSOGSA) and the nonlinear multi-layer perceptron neural network (NMLPNN), were proposed for the short-term power load forecasting. Firstly, the LDS, which guarantees that the input and output data have the same properties to ensure abundant performance. Then, the SSA technique is used for identifying and extracting the trend and seasonality of power load time series. Finally, the NMLPNN, which is optimized by the APSO, GSA, and APSOGSA, is utilized to deal with the irregularity and volatility of the power load. These integrated methods are applied to forecast half-hour power load data from New South Wales, Queensland and Singapore. By comparison of the obtained experimental results, the proposed SSA-APSOGSA-NMLPNN integrated method indicates the superiority and promising performance and has a good robustness.