A Smart Forecasting Approach to District Energy Management

Baris Yuce,Monjur Mourshed,Yacine Rezgui

doi:10.3390/en10081073

Baris Yuce, Monjur Mourshed + Show 1 more

Open Access

https://doi.org/10.3390/en10081073

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Journal: Energies	Publication Date: Jul 25, 2017
Citations: 23	License type: CC BY 4.0

Affiliation: Centre for Sustainable Energy, Cardiff University

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.

Highlights

Sustainable generation and supply of energy has become one of the biggest challenges faced by policy makers, scientists, and researchers [1], primarily because of both an increase in energy demand and the technological improvements required to respond effectively to this growth in demand
The results indicate that the Artificial Neural Networks (ANNs)-Genetic Algorithm (GA) predicts the load better than the statistical approaches
The correlation between the predicted demand and expected electricity consumption are statistically analysed using Pearson correlation coefficient and regression analysis

Summary

Introduction

Sustainable generation and supply of energy has become one of the biggest challenges faced by policy makers, scientists, and researchers [1], primarily because of both an increase in energy demand and the technological (infrastructure) improvements required to respond effectively to this growth in demand. While new decentralised micro-grids are required to be part of the low-and-medium-voltage (LV/MV) electricity grids [5,6], the centralised and federation-based grid management approach offers an efficient control of the entire electricity grid [7]. Traditional electricity grids are static systems; they do not provide detailed information about energy consumption on the demand side, making it difficult to address peak consumptions [8]. Both consumer behaviour and electricity markets are evolving rapidly, progressing towards a user-centric direction—transforming the centralised, uni-directional traditional grid into a decentralised energy-sharing grid with a bi-directional flow of information and energy [6]

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