Multi-temporal dimension prediction of new energy electricity demand based on chaos-LSSVM neural network

To address the challenges of low prediction accuracy and insufficient capture of temporal dynamic variations in new energy electricity demand, this paper proposes a chaos-optimized least squares support vector machine (LSSVM) neural network model for multi-temporal and spatial forecasting. First, leveraging an edge computing framework, data collected at the metering side are processed, and redundant time records are cleaned. By integrating chaos theory with Takens' theorem, the refined data sequence undergoes phase space reconstruction, producing a new energy electricity demand dataset with spatial correlation features. In an innovative step, the spatial transformation results are used as input, combining long short-term memory (LSTM) networks and least squares support vector machines to construct a hybrid LSSVM neural network model for electricity demand forecasting. This enables accurate and dynamic multi-temporal and spatial prediction of new energy electricity demand. Experimental results show that the proposed method achieves an MAE of 0.355 kWh and a MAPE of 1.32% for short-term new energy electricity demand forecasting, while for mid-term forecasting, the MAE and MAPE reach 25.36 kWh and 2.15%, respectively. These results verify the robustness and accuracy of the proposed method in dynamic multi-temporal and spatial electricity demand prediction.