SAGANConvLSTM: A novel spatio-temporal forecasting approach combining semivariogram-enhanced GAN and ConvLSTM for power load forecasting

In power distribution networks, spatio-temporal load forecasting plays a crucial role in decision-making and the development of distribution networks. Accurate forecasting models are essential to handle the complex dependencies in power consumption across different districts of megacities. However, the presence of missing values in smart meter data often caused by device malfunctions or communication failures, can significantly degrade model performance by increasing complexity and reducing forecasting accuracy. To address this challenge, this paper presents a novel forecasting approach named SAGANConvLSTM based on Spatial Autocorrelation (SA) utilizing spatio-temporal semivarigram and Generative Adversarial Network (GAN) to combine spatial statistics and deep learning to impute missing values in power load time series. The semivariogram quantifies spatial and temporal dependencies among substations and guides the GAN to reconstruct missing data while preserving realistic spatio-temporal structures. After imputation, the refined time series is processed by a Convolutional Long Short-Term Memory (ConvLSTM) network to generate short-term power load forecasts.The proposed model was evaluated using data from Tehran's power distribution network, achieving a mean absolute error (MAE) of 10.05 % and root mean square error (RMSE) of 15.46 %, outperforming other models like GRU, LSTM, ConvLSTM, and SA-ConvLSTM in forecasting power load over a 20-day period.