Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks
Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/5/2/21 |
| _version_ | 1797594817288470528 |
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| author | Carla Sahori Seefoo Jarquin Alessandro Gandelli Francesco Grimaccia Marco Mussetta |
| author_facet | Carla Sahori Seefoo Jarquin Alessandro Gandelli Francesco Grimaccia Marco Mussetta |
| author_sort | Carla Sahori Seefoo Jarquin |
| collection | DOAJ |
| description | Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well. |
| first_indexed | 2024-03-11T02:27:54Z |
| format | Article |
| id | doaj.art-a96e888c11704391911d5044da940738 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-11T02:27:54Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-a96e888c11704391911d5044da9407382023-11-18T10:25:38ZengMDPI AGForecasting2571-93942023-04-015239040410.3390/forecast5020021Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural NetworksCarla Sahori Seefoo Jarquin0Alessandro Gandelli1Francesco Grimaccia2Marco Mussetta3Department of Energy, Politecnico di Milano, 20133 Milan, ItalyDepartment of Energy, Politecnico di Milano, 20133 Milan, ItalyDepartment of Energy, Politecnico di Milano, 20133 Milan, ItalyDepartment of Energy, Politecnico di Milano, 20133 Milan, ItalyUnderstanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well.https://www.mdpi.com/2571-9394/5/2/21energy forecastingprobabilistic forecastingtime series analysissingular value decompositionclustering |
| spellingShingle | Carla Sahori Seefoo Jarquin Alessandro Gandelli Francesco Grimaccia Marco Mussetta Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks Forecasting energy forecasting probabilistic forecasting time series analysis singular value decomposition clustering |
| title | Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks |
| title_full | Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks |
| title_fullStr | Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks |
| title_full_unstemmed | Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks |
| title_short | Short-Term Probabilistic Load Forecasting in University Buildings by Means of Artificial Neural Networks |
| title_sort | short term probabilistic load forecasting in university buildings by means of artificial neural networks |
| topic | energy forecasting probabilistic forecasting time series analysis singular value decomposition clustering |
| url | https://www.mdpi.com/2571-9394/5/2/21 |
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