ForecastExplainer: Explainable household energy demand forecasting by approximating shapley values using DeepLIFT

The rapid progress in sensor technology has empowered smart home systems to efficiently monitor and control household appliances. AI-enabled smart home systems can forecast household future energy demand so that the occupants can revise their energy consumption plan and be aware of optimal energy consumption practices. However, deep learning (DL)-based demand forecasting models are complex and decisions from such black-box models are often considered opaque. Recently, eXplainable Artificial Intelligence (XAI) has garnered substantial attention in explaining decisions of complex DL models. The primary objective is to enhance the acceptance, trust, and transparency of AI models by offering explanations about provided decisions. We propose ForecastExplainer, an explainable deep energy demand forecasting framework that leverages Deep Learning Important Features (DeepLIFT) to approximate Shapley values to map the contribution of different appliances and features with time. The generated explanations can shed light to explain the prediction highlighting the impact of energy consumption attributes corresponding to time, such as responsible appliances, consumption by household areas and activities, and seasonal effects. Experiments on household datasets demonstrated the effectiveness of our method in accurate forecasting. We designed a new metric to evaluate the effectiveness of the generated explanations and the experiment results indicate the comprehensibility of the explanations. These insights might empower users to optimize energy consumption practices, fostering AI adoption in smart applications.