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Incorporating causality in energy consumption forecasting using deep neural networks
Kshitij Sharma,
Yogesh Dwivedi,
Bhimaraya Metri
Annals of Operations Research
Swansea University Author: Yogesh Dwivedi
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DOI (Published version): 10.1007/s10479-022-04857-3
Abstract
Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advan...
| Published in: | Annals of Operations Research |
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| ISSN: | 0254-5330 1572-9338 |
| Published: |
Springer Science and Business Media LLC
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60324 |
| Abstract: |
Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems. |
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| Keywords: |
Deep neural networks; Energy consumption; Forecasting; Machine learning |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Swansea University |