A Hybrid Anomaly-Filtered Spatio-Temporal Framework for Robust Energy Demand Forecasting in Solar-Integrated Smart Grids

Abstract

The study introduces the Hybrid Anomaly-Filtered Spatio-Temporal Representation Network (HASTRN) to achieve accurate forecasting of short-term and day-ahead energy demand by utilizing smart-meter, solar PV, and weather-integrated data. Energy demand forecasting in residential buildings has become increasingly difficult due to varying loading patterns, solar irregularities, and the complexity of nonlinear relationships that exist between energy consumption and weather parameters. Conventional statistical and AI-based models have demonstrated a low capacity for managing anomalies, missing values, and temporal dependencies in the long run. To overcome these issues, the present paper creates a new hybrid approach to the problem, which combines multi-stage anomaly detection (Z-score, IQR, sliding-window), the Hybrid Correlation SVM Feature-Optimization Technique (HCSFOT) to perform advanced feature engineering, and a unified Deep Adaptive Spatio-Temporal Learning Network (DASTLN) network, which a combined Deep Neural Network (DNN), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) architecture to predict the energy demand. The data acquisition, preprocessing, feature optimization, development of hybrid models, and multi-horizon forecasting are the workflow components. The hierarchical nonlinear interactions, instantaneous load variations, and long-range temporal trends are well represented by the proposed HASTRN model. From the experiments’ outcomes, one can clearly see the superiority of the forecasting approach with respect to RMSE, MAE, MAPE, R2 and solar variance metrics, indicating the effectiveness of the suggested HASTRN algorithm. The anticipated results are an increased stability of forecasting in the case of intermittency due to the sun, increased robustness in the presence of missing data, and an improved ability to manage the grid. The results make the HASTRN a good candidate for the next generations of smart energy-management systems.