Hybrid Machine Learning for Time-Series Energy Data for Enhancing Energy Efficiency in Buildings

• Published in: Computational Science – ICCS 2021 pp. 273 - 285
• Main Authors: Ngo, Ngoc-Tri, Pham, Anh-Duc, Truong, Ngoc-Son, Truong, Thi Thu Ha, Huynh, Nhat-To
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Data analytics; Energy consumption data; Machine learning; Prediction model
• ISBN: 3030779769; 9783030779764
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-77977-1_21

Buildings consume about 40% of the world's energy use. Energy efficiency in buildings is an increasing concern for the building owners. A reliable energy use prediction model is crucial for decision-makers. This study proposed a hybrid machine learning model for predicting one-day-ahead time-series electricity use data in buildings. The proposed SAMFOR model combined support vector regression (SVR) and firefly algorithm (FA) with conventional time-series seasonal autoregressive integrated moving average (SARIMA) forecasting model. Large datasets of electricity use in office buildings in Vietnam were used to develop the forecasting model. Results show that the proposed SAMFOR model was more effective than the baselines machine learning models. The proposed model has the lowest errors, which yielded 0.90 kWh in RMSE, 0.96 kWh in MAE, 9.04% in MAPE, 0.904 in R in the test phase. The prediction results provide building managers with useful information to enhance energy-saving solutions.