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A Smart Forecasting Approach to District Energy Management

Author

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  • Baris Yuce

    (BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
    College of Engineering, Mathematics, and Physical Sciences, School of Engineering, Streatham Campus University of Exeter, Exeter EX4 4QJ, UK
    The research reported in this study was conducted while Baris Yuce was affiliated with Cardiff University.)

  • Monjur Mourshed

    (BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Yacine Rezgui

    (BRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

Abstract

This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers’ employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

Suggested Citation

  • Baris Yuce & Monjur Mourshed & Yacine Rezgui, 2017. "A Smart Forecasting Approach to District Energy Management," Energies, MDPI, vol. 10(8), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1073-:d:105764
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    References listed on IDEAS

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    Cited by:

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    4. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    5. Jessica Hermanns & Marcel Modemann & Kamil Korotkiewicz & Frederik Paulat & Kevin Kotthaus & Sven Pack & Markus Zdrallek, 2020. "Evaluation of Different Development Possibilities of Distribution Grid State Forecasts," Energies, MDPI, vol. 13(8), pages 1-17, April.
    6. Sébastien Bissey & Sébastien Jacques & Jean-Charles Le Bunetel, 2017. "The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing," Energies, MDPI, vol. 10(11), pages 1-24, October.
    7. Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.
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    10. Sarah Hadri & Mehdi Najib & Mohamed Bakhouya & Youssef Fakhri & Mohamed El Arroussi, 2021. "Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.

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