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Forecasting Electricity and District Heating Consumption: A Case Study in Schools in Sweden
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Infrastructure, IT Department, Skellefteå Municipality, Sweden.ORCID iD: 0000-0002-5704-4667
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
2023 (English)In: 2023 IEEE Green Technologies Conference, GreenTech, IEEE , 2023, p. 169-175Conference paper, Published paper (Refereed)
Abstract [en]

The growing population and demand for new public buildings contribute to increased energy consumption and greenhouse emissions. In Sweden, the largest amount of energy is consumed in school buildings, i.e., where schools form the highest number of public properties (30 million m2). In total, schools consumed 4 222 GWh of district heating and about 3 GWh of electricity for heating and other purposes in 2020. These figures lead to the realization of the need to apply effective measures to meet the European Green Deal target for 2030. Accurately forecasting energy usage is important for all stakeholders to conduct economic analysis and optimize decision-making. It is equally important in maintenance operations to allocate resources and enable the staff and students to adjust their behaviours and address the issues in buildings where peak forecasts occur. This paper develops and evaluates a power and district heating consumption for a single day and multiple days forecasting using Multivariate Recurrent Neural Network (RNN)-Long-Short term memory (LSTM) and convolutional neural networks (CNNs) and Autoencoders (AE), using daily real consumption data of six public schools provided by Skelefteå municipality in Sweden. The experimental results demonstrate that the hybrid model CNN-LSTM has achieved good accuracy compared to others, with RMSE and nRMSE error between 18%-25% and 5%-6% for electricity, respectively, and between 20%-30% RMSE and 5% nRMSE for district heating.

Place, publisher, year, edition, pages
IEEE , 2023. p. 169-175
Series
IEEE Green Technologies Conference, ISSN 2166-546X, E-ISSN 2166-5478
Keywords [en]
AE, CNN-LSTM, district heating, electricity, Forecasting consumption, LSTM, school buildings, time series analysis
National Category
Energy Engineering Energy Systems
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-100641DOI: 10.1109/GreenTech56823.2023.10173792ISI: 001043037200033Scopus ID: 2-s2.0-85166255803ISBN: 978-1-6654-9287-4 (electronic)ISBN: 978-1-6654-9288-1 (print)OAI: oai:DiVA.org:ltu-100641DiVA, id: diva2:1788849
Conference
15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2024-04-12Bibliographically approved

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Shahid, Zahraa KhaisSaguna, SagunaÅhlund, Christer

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