Causally explainable artificial intelligence on deep learning model for energy demand prediction

Accurate power demand prediction is essential for energy management in the energy sector, but it is difficult due to the factors such as greenhouse gas emissions and climate change. CNN-LSTM (Convolutional neural network-long short-term memory) neural network has demonstrated impressive performance but faces limitations in explaining its prediction results. Although XAI (explainable artificial intelligence) techniques enhance understanding through feature importance, they primarily focus on correlation rather than causality among variables in the deep learning models. To address this issue, we propose a causal XAI method for CNN-LSTM neural network with attention mechanism to predict power demand. Bayesian network is employed to provide the causal explanation with domain knowledge and relationships among observed variables and deep learning parameters (e.g., class activation maps and attention weights). Experiments on two real datasets such as UCI (University of California, Irvine) individual household electricity dataset and REFIT (Regulatory Fitness and Performance programme of the European Commission) dataset show improvements of average 34.84 % and 13.63 %, respectively. It also confirms that the proposed method not only significantly outperforms state-of-the-art models in terms of prediction accuracy but also provides a causal explanation of the prediction outcome in terms of peak power usage, savings, and stability based on the observation windows, which provides actionable insight for end users to achieve power efficiency.