Machine learning for energy consumption prediction and scheduling in smart buildings
| Autor: | Najib Elkamoun, Safae Bourhnane, Rachid Lghoul, Driss Benhaddou, Mohamed Riduan Abid, Khalid Zine-Dine |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry General Chemical Engineering Testbed General Engineering General Physics and Astronomy Energy consumption Machine learning computer.software_genre Scheduling (computing) Smart grid Management system CompactRIO General Earth and Planetary Sciences General Materials Science Artificial intelligence business computer General Environmental Science Building automation |
| Zdroj: | SN Applied Sciences. 2 |
| ISSN: | 2523-3971 2523-3963 |
| DOI: | 10.1007/s42452-020-2024-9 |
| Popis: | Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling. |
| Databáze: | OpenAIRE |
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