Zhang, M., Millar, M.-A., Chen, S., Ren, Y., Yu, Z. and Yu, J. (2024) Enhancing hourly heat demand prediction through artificial neural networks: a national level case study. Energy and AI, 15, 100315. (doi: 10.1016/j.egyai.2023.100315)
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Abstract
Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption, which is increasingly important. However, conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale, high-resolution, and fast forecasting due to their complexity and the difficulty in obtaining model parameters. This paper presents an artificial neural network (ANN) model to predict hourly heat demand on a national level, which replaces the traditional bottom-up model based on extensive building simulations and computation. The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection, making the model more realistic by removing non-essential inputs. The improved model can be trained using fewer meteorological data types and insufficient data, while accurately forecasting the hourly heat demand throughout the year within an acceptable error range. The model provides a framework to obtain accurate heat demand predictions for large-scale areas, which can be used as a reference for stakeholders, especially policymakers, to make informed decisions.
| Item Type: | Articles |
|---|---|
| Additional Information: | This research has benefited from the financial support provided by EPSRC (EP/T022701/1, EP/V042033/1, EP/V030515/1, EP/W027593/1) in the UK. Author Meng Zhang likes to acknowledge Scottish Power Energy Networks for the PhD studentship. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Millar, Michael-Allan and Zhang, Meng and Ren, Dr Yaxing and Chen, Dr Si and Yu, Professor Zhibin |
| Authors: | Zhang, M., Millar, M.-A., Chen, S., Ren, Y., Yu, Z., and Yu, J. |
| College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Systems Power and Energy |
| Journal Name: | Energy and AI |
| Publisher: | Elsevier |
| ISSN: | 2666-5468 |
| ISSN (Online): | 2666-5468 |
| Published Online: | 04 November 2023 |
| Copyright Holders: | Copyright © 2023 The Authors |
| First Published: | First published in Energy and AI 15: 100315 |
| Publisher Policy: | Reproduced under a Creative Commons license |
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