Explainable Artificial Intelligence Approach for Demand-Side Management in a 1-Phase Multi-Type Consumer Base: Enhancing Efficiency and Transparency

Abstract

Technological advancements have enabled electricity utilities to experiment with various artificially intelligent approaches to minimize the challenges posed by end-user demand volatility. Although the introduction of such techniques has made operating the system easier, it has also made the internal process difficult to interpret. It makes difficult for the operator to solve any issues raised due to any fault in the model design. Designing demand response strategies that are simple to comprehend is crucial for this reason. Hence, the consumer demand response model will exhibit the much-needed system behavior of transparency, trust, and objectivity. The fundamental goal of this research is to introduce an explainable artificially intelligent (XAI) demand response (DR) model based on machine learning (ML) that assures supply-demand equilibrium across the power system network. The proposed methodology combines an integrated load forecasting approach with a DR model based on Jaya optimization. Subsequently, the effectiveness of the DR program is illustrated in relation to the accuracy of the load forecasting model. The operation of this integrated ML-based technique was shown using an XAI-based model architecture. The proposed technique was modelled and tested in the MATLAB interface utilizing a database of 24 end-user energy usage.