Smart City Gnosys
Smart city article details
| Title | Energy Forecasting In A Public Building: A Benchmarking Analysis On Long Short-Term Memory (Lstm), Support Vector Regression (Svr), And Extreme Gradient Boosting (Xgboost) Networks |
|---|---|
| ID_Doc | 23280 |
| Authors | Huang J.; Algahtani M.; Kaewunruen S. |
| Year | 2022 |
| Published | Applied Sciences (Switzerland), 12, 19 |
| DOI | http://dx.doi.org/10.3390/app12199788 |
| Abstract | A primary energy consumption and CO2 emission source stems from buildings and infrastructures due to rapid urbanisation and social development. An accurate method to forecast energy consumption in a building is thus critically needed to enable successful management of adaptive energy consumption and ease the level of CO2 emission. However, energy forecasting for buildings, especially residential buildings, has several challenges, such as significant variations in energy usage patterns due to unpredicted demands of the residences and some intricate factors, which can randomly affect the patterns. Traditional forecasting approaches require a tremendous number of inputs needed for building physic models and variations often exist between as-built and as-designed buildings in reality. Most recent studies have adopted only ambient weather conditions, building components, and the occupant’s behaviours. As a result, in order to take into account the complexity of factors that can affect the building energy model development and its computation, we develop advanced machine learning models driven by the inherent electricity consumption pattern associated with the day and time. In this study, we demonstrate benchmarking results derived from three different machine learning algorithms, namely SVR, XGBoost, and LSTM, trained by using 1-year datasets with sub-hourly (30 min) temporal granularity to determine the outperformed predictor. Ultimately, the machine learning model robustness and performance on a basis of the coefficient of variation (CV) obtained by the SVR is benchmarked across XGBoost and LSTM trained by the same datasets containing attributes related to the building type, data size, and temporal granularity. The insight stemming from this study indicates that the suitable choice of the machine learning models for building energy forecasts largely depends on the natural characteristics of building energy data. Hyperparameter tuning or mathematical modification within an algorithm may not be sufficient to attain the most accurate machine learning model for building energy forecast. © 2022 by the authors. |
| Author Keywords | AI model; building energy; CO<sub>2</sub> emissions; energy consumption; machine learning; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
762  | 0.929 | Wu J.; Nguyen S.; Alahakoon D.; De Silva D.; Mills N.; Rathnayaka P.; Moraliyage H.; Jennings A. | A Comparative Analysis Of Machine Learning-Based Energy Baseline Models Across Multiple Building Types | Energies, 17, 6 (2024) |
| 11151 | 0.922 | Yang F.; Mao Q. | Auto-Evaluation Model For The Prediction Of Building Energy Consumption That Combines Modified Kalman Filtering And Long Short-Term Memory | Sustainability (Switzerland), 15, 22 (2023) |
| 57903 | 0.921 | Daniel H.; Mantha B.R.K.; Soto B.G.D. | Towards A Review Of Building Energy Forecast Models | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019) |
| 54322 | 0.918 | Ardabili S.; Abdolalizadeh L.; Mako C.; Torok B.; Mosavi A. | Systematic Review Of Deep Learning And Machine Learning For Building Energy | Frontiers in Energy Research, 10 (2022) |
| 52403 | 0.905 | Roth J.; Bailey A.; Choudhary S.; Jain R.K. | Spatial And Temporal Modeling Of Urban Building Energy Consumption Using Machine Learning And Open Data | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019) |
| 17892 | 0.903 | Moveh S.; Merchán-Cruz E.A.; Abuhussain M.; Dodo Y.A.; Alhumaid S.; Alhamami A.H. | Deep Learning Framework Using Transformer Networks For Multi Building Energy Consumption Prediction In Smart Cities | Energies, 18, 6 (2025) |
| 32444 | 0.898 | Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A. | Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency | Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023) |
| 36066 | 0.895 | Ajagunsegun T.; Li J.; Bamisile O.; Ohakwe C. | Machine Learning-Based System For Managing Energy Efficiency Of Public Buildings: An Approach Towards Smart Cities | 2022 4th Asia Energy and Electrical Engineering Symposium, AEEES 2022 (2022) |
| 28891 | 0.895 | Akbarzadeh O.; Hamzehei S.; Attar H.; Amer A.; Fasihihour N.; Khosravi M.R.; Solyman A.A. | Heating-Cooling Monitoring And Power Consumption Forecasting Using Lstm For Energy-Efficient Smart Management Of Buildings: A Computational Intelligence Solution For Smart Homes | Tsinghua Science and Technology, 29, 1 (2024) |
| 13024 | 0.894 | Ardabili S.; Mosavi A.; Várkonyi-Kóczy A.R. | Building Energy Information: Demand And Consumption Prediction With Machine Learning Models For Sustainable And Smart Cities | Lecture Notes in Networks and Systems, 101 (2020) |