CHEFS: Explanable DL and Edge-Based Power Consumption Analysis Framework for Smart Homes

The growing use of smart technologies in homes has changed the way we manage power consumption. In this paper, CHEFS an innovative framework is proposed for smart home power consumption analysis, using Explainable AI (XAI) to improve clarity and accuracy with prediction and continuous decision-making by edge computing. Devices like the Internet of Things (IoT) and smart meters are used in smart homes to observe energy usage precisely. Such devices are also used for cutting energy costs and reducing environmental impacts. They often lack clarity in decision-making which makes it difficult, whereas the traditional machine learning models offer accurate predictions. To show this, we use XAI methods like SHAP and LIME to get a better understanding of the power usage patterns and forecasts. Edge computing reduces delays and bandwidth usage while also enhancing response time. This approach enhances clarity and dependability in Artificial Intelligence (AI) models providing reliable insights into household power consumption. A comprehensive evaluation helps create a more transparent and effective system for creating smarter energy solutions.