An improved WT and NN ensemble demand forecast model for PV integrated smart buildings

Raza, Muhammad Qamar, Nadarajah, Mithulananthan and Ekanayake, Chandima (2016). An improved WT and NN ensemble demand forecast model for PV integrated smart buildings. In: IEEE PES Innovative Smart Grid Technologies Conference Europe. 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016, Melbourne, Australia, (781-786). 28 November - 1 December 2016. doi:10.1109/ISGT-Asia.2016.7796484


Author Raza, Muhammad Qamar
Nadarajah, Mithulananthan
Ekanayake, Chandima
Title of paper An improved WT and NN ensemble demand forecast model for PV integrated smart buildings
Conference name 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
Conference location Melbourne, Australia
Conference dates 28 November - 1 December 2016
Convener IEEE
Proceedings title IEEE PES Innovative Smart Grid Technologies Conference Europe
Journal name IEEE PES Innovative Smart Grid Technologies Conference Europe
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2016
Sub-type Fully published paper
DOI 10.1109/ISGT-Asia.2016.7796484
Open Access Status Not yet assessed
ISBN 9781509043033
Start page 781
End page 786
Total pages 6
Collection year 2017
Language eng
Abstract/Summary Buildings are one of the major sources of greenhouse gas emissions and electricity consumption in urban areas all around the world. The load demand of large buildings is highly uncertain due to large penetration of solar PV. As a result, it leads to serious power system stability and quality issues for network operators and energy managers. Therefore, accurately forecast the load demand of buildings is utmost important to design a better energy management system and reduce greenhouse gas emission. However, variable PV output power, random nature of weather, diversity and complexity of buildings are big hurdles to accurately predict the load demand. In this paper, a deterministic hybrid intelligent forecast framework is proposed based on a combination of cascade-forward back-propagation network (CFBPN) in ensemble network for accurate load demand forecast. The wavelet transform (WT) technique is applied to handle the sharp spikes and fluctuations in historical load demand data. In addition, particle swarm optimization (PSO) is used to train the CFBPN in ensemble network for better forecast accuracy. In proposed forecast framework, historical load demand data, temperature, wind speed and humidity are applied as ensemble model inputs. The performance of proposed model is analyzed for one day and 12 hours ahead load demand forecast of summer (S), autumn (A), winter (W) and spring (SP) days. The proposed forecast model provides higher forecast accuracy compared to autoregressive (AR) and wavelet transformed backpropagation neural network (WT+BPNN).
Keyword Deamand forecast
Neural network ensbmle (NNE)
Particle swarm optimization (PSO.)
Smart buildings
Wavelet transform (WT)
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Tue, 31 Jan 2017, 00:21:27 EST by System User on behalf of Learning and Research Services (UQ Library)