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Abbas Khosravi
Researcher at Deakin University
Publications - 353
Citations - 11323
Abbas Khosravi is an academic researcher from Deakin University. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 42, co-authored 305 publications receiving 7313 citations. Previous affiliations of Abbas Khosravi include Cooperative Research Centre & University of Girona.
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A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
TL;DR: In this article, the authors provide a comprehensive and systematic literature review of Artificial Intelligence based short-term load forecasting techniques and provide the major objective of this study is to review, identify, evaluate and analyze the performance of artificial Intelligence based load forecast models and research gaps.
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Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals
TL;DR: A new, fast, yet reliable method for the construction of PIs for NN predictions, and the quantitative comparison with three traditional techniques for prediction interval construction reveals that the LUBE method is simpler, faster, and more reliable.
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Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
TL;DR: A neural network (NN)-based method for the construction of prediction intervals (PIs) and a new problem formulation is proposed, which translates the primary multiobjectives problem into a constrained single-objective problem.
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Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
TL;DR: The quality of PIs produced by the combiners is dramatically better than the quality ofPIs obtained from each individual method and a new method for generating combined PIs using the traditional PIs is proposed.
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Construction of Optimal Prediction Intervals for Load Forecasting Problems
TL;DR: In this article, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability, and simulated annealing is used for minimization of this cost function and adjustment of neural network parameters.