Just In Time Transformers
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915030503522304 |
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| author | Benali, Ahmed Ala Eddine Cafaro, Massimo Epicoco, Italo Pulimeno, Marco Schioppa, Enrico Junior |
| author_facet | Benali, Ahmed Ala Eddine Cafaro, Massimo Epicoco, Italo Pulimeno, Marco Schioppa, Enrico Junior |
| contents | Precise energy load forecasting in residential households is crucial for mitigating carbon emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility companies and policymakers, who advocate sustainable energy practices, to optimize resource utilization. Moreover, smart meters provide valuable information by allowing for granular insights into consumption patterns. Building upon available smart meter data, our study aims to cluster consumers into distinct groups according to their energy usage behaviours, effectively capturing a diverse spectrum of consumption patterns. Next, we design JITtrans (Just In Time transformer), a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy, with respect to traditional forecasting methods. Extensive experimental results validate our claims using proprietary smart meter data. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems: the development of efficient and eco-friendly energy solutions critically depends on such technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16881 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Just In Time Transformers Benali, Ahmed Ala Eddine Cafaro, Massimo Epicoco, Italo Pulimeno, Marco Schioppa, Enrico Junior Machine Learning 68T07 I.2; I.2.6 Precise energy load forecasting in residential households is crucial for mitigating carbon emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility companies and policymakers, who advocate sustainable energy practices, to optimize resource utilization. Moreover, smart meters provide valuable information by allowing for granular insights into consumption patterns. Building upon available smart meter data, our study aims to cluster consumers into distinct groups according to their energy usage behaviours, effectively capturing a diverse spectrum of consumption patterns. Next, we design JITtrans (Just In Time transformer), a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy, with respect to traditional forecasting methods. Extensive experimental results validate our claims using proprietary smart meter data. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems: the development of efficient and eco-friendly energy solutions critically depends on such technologies. |
| title | Just In Time Transformers |
| topic | Machine Learning 68T07 I.2; I.2.6 |
| url | https://arxiv.org/abs/2410.16881 |