Power System Load Forecasting Enhanced by Adaptive Memory-Synthesis Network

In today’s power system load forecasting landscape, traditional forecasting meth- ods for power system load data are increasingly inadequate for addressing the complexities of modern energy markets. For instance, factors such as seasonal variations in energy consumption, daily usage patterns, and the integration of renewable energy sources introduce significant challenges in capturing the nonlin- ear dynamics inherent in power load forecasting. Furthermore, external influences like policy-driven electricity price fluctuations add another layer of complexity that traditional models struggle to incorporate effectively. Conventional neural networks, in particular, often fail to account for these dynamic effects, resulting in prediction errors that can exceed 15% during periods of high volatility. To overcome these limitations, this study introduces a novel hybrid model, adaptive memory-synthesis network, which combines LSTM’s ability to capture short- term dependencies with Transformer’s capacity to model long-range correlations in complex time series data. Experimental evaluations using the State Grid Cor- poration of China (SGCC) power system load dataset demonstrate that the adaptive memory-synthesis network model achieves up to a 27% improve- ment in forecasting accuracy compared to traditional neural network approaches. Additionally, the model enhances operational effciency by 16%, with a Mean Absolute Percentage Error (MAPE) of 8.95%, offering the potential to signifi- cantly reduce unnecessary maintenance costs for large-scale grid operators on an annual basis.