Prediction of peak energy demand and timestamping in commercial supermarkets using deep learning
Abstract
Peak demand consumption is an ongoing research topic due to current environmental concerns. Accurate prediction of peak demand leads to improved power storage scheduling and smart grid management. However, existing researches on peak demand in commercial buildings lack focus on the timestamps for the incident of peak consumption. For this reason, this research proposes to label three indexes per day and use the novel Energy Peaks and Timestamping Prediction (EPTP) framework to detect the energy peaks as well as the timestamps for the occurring indexes.
The EPTP framework is proposed with three phases. In the first phase, data preprocessing cleans the raw data into the intended input for the deep learning model, and timestamp labelling creates the expected output for training and evaluation of the model. The second phase focuses on energy consumption prediction using Long Short-Term Memory (LSTM) network, which is dedicated to processing sequential data. The last phase uses Multilayer Perceptron (MLP) for the purpose of timestamp prediction. The EPTP framework is evaluated using various data resolutions and compared to the common label of using block maxima from extreme value theory. Specifically, the two-hour hit rate improves from 21\% using the block maxima approach to 52.6\% with the proposed EPTP framework, and from 65.3\% to 86\% for the 1-hour resolution and the 15-minute resolution, respectively. In addition, the average minute deviation decreases from 120 minutes using the block maxima approach to 62 minutes with the proposed EPTP framework for the high-resolution data. The framework shows adequate results from high-resolution data using real-world commercial supermarket energy consumption.