SmartGridOptimizer-X: A Novel Energy-Efficient Design Framework for Sustainable Electrical Systems Integration
Ramkumar G
Abstract
The SmartGRidOptimizer-X is an advanced hybrid forecasting model that can precisely forecast the energy demand by incorporating Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks, and Adaptive Gradient Boosting Machines (Adaptive-GBMs). The model is also able to tackle the issues of complex energy systems by embracing multi-scale temporal dependencies and capitalizing on contextual aspects of weather, socio-economic, and historical trends. CNLSTM and Adaptive-GBM are used with 75 and 25 percent as optimized ensemble weights, respectively, making it stronger and more accurate. The system was tested aggressively on the Energy Prediction Smart-Meter Dataset with an incredible accuracy of 98.43% (R2 = 0.9843), Mean Absolute Error (MAE) was 0.089 kWh, and Root Mean Squared Error (RMSE) was 0.115 kWh. The model proved to be flexible under seasonal performance metrics, and the values of R2 were 0.966 in extreme weather and 0.994 in summer. The model accuracy in detecting anomalies in energy usage was 96.3% and a recall of 94.5%, which validates the model's capability to detect patient abnormal energy use trends. The SmartGridOptimizer-X framework is a breakthrough in energy forecasting ever made and offers utility providers an efficient tool for managing the grid, load balancing, and demand response strategies.