A new customer selection framework for time-based pricing program.

Time-based pricing for residents is regarded as a critical component to facilitate integration of renewable energy, but it remains one of the significant barriers to target high potential customers. Randomly recruiting customers into time-based pricing may lead to customers' bad experiences and utility companies' economic losses. Focusing on this issue, this paper proposes a new customer selection framework for time-based pricing using the smart meter data. To utilize smart meter data effectively, a novel feature engineering procedure is designed, covering both of statistical characteristics and load patterns. Then, a Bayesian neural network (BNN) based prediction model is developed by integrating the Bayesian theory into neural networks for identifying high-potential customers. Finally, the prediction uncertainty is quantified to help utility companies to identify when to trust the prediction results. Case studies on an actual time-based pricing pilot demonstrate the effectiveness of the proposed framework. As for the comprehensive metric F1-score, BNN is in the lead, which is 9.0 % higher than neural network, 6.0 % higher than convolutional neural network (CNN), and 3.3 % higher than long short-term memory (LSTM). In addition, by leveraging the quantified uncertainties, the percentage of low-potential households among selected customers drops by 8.92 %, compared to blindly trusting the prediction results. Furthermore, it's found that load pattern features contribute more to model performance than statistical features, revealing that it's the shape, rather than the amplitude, of daily load curves that determines residential price responsiveness. © 2017 Elsevier Inc. All rights reserved. • A novel feature engineering approach is proposed to dig out information from smart meter data. • A Bayesian neural network-based prediction model is developed to identify customers with great price responsiveness. • The prediction uncertainty is quantified to determine when to trust the prediction results. • The effectiveness of the proposed customer selection framework is validated on an actual time-based pricing dataset. [ABSTRACT FROM AUTHOR]