Energy Consumption Forecasting for the Digital-Twin Model of the Building

• Published in: Energies (Basel) 2022-06, Vol.15 (12), p.4318
• Main Authors: Henzel, Joanna, Wróbel, Łukasz, Fice, Marcin, Sikora, Marek
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Buildings; COVID-19; Datasets; Deep learning; Digital twins; digital-twin model; Electricity; Electricity distribution; Energy consumption; energy consumption forecasting; Forecasting; Neural networks; Regression analysis; residential building energy consumption; Time series; time series forecasting; Wavelet transforms
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15124318

The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building.