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Energies 2017, 10(8), 1073;

A Smart Forecasting Approach to District Energy Management

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
Authors to whom correspondence should be addressed.
The research reported in this study was conducted while Baris Yuce was affiliated with Cardiff University.
Academic Editor: Joseph H. M. Tah
Received: 15 May 2017 / Revised: 9 July 2017 / Accepted: 14 July 2017 / Published: 25 July 2017
(This article belongs to the Special Issue Zero-Carbon Buildings)
PDF [6620 KB, uploaded 25 July 2017]


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. View Full-Text
Keywords: ANN; PCA; MRA; district energy management; smart grid; smart cities; demand forecasting ANN; PCA; MRA; district energy management; smart grid; smart cities; demand forecasting

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Yuce, B.; Mourshed, M.; Rezgui, Y. A Smart Forecasting Approach to District Energy Management. Energies 2017, 10, 1073.

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