Optimization based load forecasting and demand management in smart building microgrids with Greylag Goose and Bi level graph models

The effective use of energy in Smart Building Microgrids (SBMGs) largely depends on accurate load prediction and synchronized demand response especially with battery degradation and unreliability of renewable power. In spite of progress, problems of inaccuracies in forecasting, mismatch in demand and supply, and non-optimal optimization methods still ruin the reliability of the system and the durability of battery resources. A domain-adapted forecasting and optimization framework is proposed in this paper intending to combine Greylag Goose Optimization (GGO) with a Relational Bi-Level Aggregation Graph Convoluted Network (RBAGCN) to be used in SBMGs. In this context, the RBAGCN has been reengineered to incorporate physical and operating interrelations among the energy variables, and the GGO has been utilized to stabilize network weight convergence when the load is non-stationary, as opposed to being an independent optimizer. Before the training of a model, Fast Resampled Iterative Filtering (FRIF) is used to clean up and normalize historical sequential data and Prairie Dog Optimization (PDO) is used to remove the least salient features, i.e. three phase discharge power, battery discharge power, time of day, solar voltage, and ambient temperature. The modified RBAGCN performs load forecasting and demand-based management forecasting, and the GGO automatically adjusts the model parameters to improve convergence strength. As the simulation experiments with the MATLAB R2022b show, the proposed framework achieves an average forecasting accuracy of 98.3%, which is better than the benchmark models, including RNN (82.6%), ABMO-ANN (85.4%), RNN-LSTM (91.7%), GA-DNN (88.5%), and RNN-GRU (90.3%). In addition to this, the framework has lower error measures, such as mean absolute error of 0.0164, mean absolute percentage error of 0.0128, and mean squared error of 0.0069 as compared to benchmark values of 0.042, 0.037 and 0.031 respectively. Moreover, prediction variance and convergence iterations are decreased to 26.5% and 17.8%, respectively, which suggests a higher level of statistical stability and demand management learning efficiency of SBMG load prediction and battery-conscious demand management.