Operation Planning Method Using Convolutional Neural Network for Combined Heat and Power System

Abstract

More and more factories and commercial buildings are installing combined heat and power (CHP) systems that include various energy storage devices. To reduce the energy cost of CHPs, optimal operation plans to satisfy time‐varying energy demands with minimum energy cost are required. Conventional operation planning methods using optimized calculation have an issue with long computing time. To address this, we adapted a convolutional neural network (CNN) to learn the time‐series features of input–output pairs of optimized calculations. The architecture of the proposed CNN has the following key features: (i) the application of one‐dimensional convolutional filters to extract the time‐series features of input data and (ii) the elimination of pooling layers that compress the input data. We also designed rule‐based flows to remove constraint violations from the generated operation plans. Our evaluations showed that the proposed method could generate operation plans with the average error of 3.3%, which is much lower than the 8.0% average error when using the conventional CNN with pooling calculation. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.