A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza, Muhammad Qamar and Khosravi, Abbas 2015, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renewable and sustainable energy reviews, vol. 50, pp. 1352-1372, doi: 10.1016/j.rser.2015.04.065.

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Title A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
Author(s) Raza, Muhammad Qamar
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Journal name Renewable and sustainable energy reviews
Volume number 50
Start page 1352
End page 1372
Total pages 21
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015
ISSN 1364-0321
1879-0690
Keyword(s) Science & Technology
Technology
Energy & Fuels
Artificial intelligence (AI)
Neural network (NN)
Fuzzy logic
Short term load forecasting (STLF)
Smart grid (SG)
FEEDFORWARD NEURAL-NETWORKS
PARTICLE SWARM OPTIMIZATION
VIRTUAL POWER-PLANTS
TIME-SERIES
AC-MICROGRIDS
ARMA MODEL
ALGORITHM
SYSTEMS
BACKPROPAGATION
REGRESSION
Summary Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.
Language eng
DOI 10.1016/j.rser.2015.04.065
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
09 Engineering
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079900

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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