Kommunikationsnetze / Multimedia Kommunikation
Hybrid Model for Large Scale Forecasting of Power Consumption | |
Key: | AABR17-2 |
Author: | Wael Alkhatib, Alaa Alhamoud, Doreen Böhnstedt, Ralf Steinmetz |
Date: | June 2017 |
Kind: | In proceedings |
Publisher: | Springer, Cham |
Book title: | International Work-Conference on Artificial Neural Networks |
Keywords: | Smart grid; Multiple linear regression; Time series models; Random forests; Conditional inference trees. |
Abstract: | After the electricity liberalization in Europe, the electricity market moved to a more competitive supply market with higher efficiency in power production. As a result of this competitiveness, accurate models for forecasting long-term power consumption become essential for electric utilities as they help operating and planning of the utility's facilities including Transmission and Distribution (T&D) equipments. In this paper, we develop a multi-step statistical analysis approach to interpret the correlation between power consumption of residential as well as industrial buildings and its main potential driving factors using the dataset of the Irish Commission for Energy Regulation (CER). In addition we design a hybrid model for forecasting long-term daily power consumption on the scale of portfolio of buildings using the models of conditional inference trees and linear regression. Based on an extensive evaluation study, our model outperforms two robust machine learning algorithms, namely random forests (RF) and conditional inference tree (ctree) algorithms in terms of time eciency and prediction accuracy for individual buildings as well as for a portfolio of buildings. The proposed model reveals that dividing buildings in homogeneous groups, based on their characteristics and inhabitants demographics, can increase the prediction accuracy and improve the time efficiency. |
The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, not withstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.