Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting-Reference-Cited by-同舟云学术

Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting

Published:2019-12-02 Issue:10-12 Volume:14 Page:341-358
ISSN:1556-7249
Container-title:Energy Sources, Part B: Economics, Planning, and Policy
language:en
Short-container-title:Energy Sources, Part B: Economics, Planning, and Policy

Author:

Heydari Azim¹^ORCID,Astiaso Garcia Davide¹,Keynia Farshid²,Bisegna Fabio¹,De Santoli Livio¹

Affiliation:

1. Department of Astronautical, Electrical and Energy Engineering (DIAEE) Sapienza University, Rome, Italy

2. Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

Publisher

Informa UK Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15567249.2020.1717678

Reference42 articles.

1. Estimation of electricity demand of Iran using two heuristic algorithms

2. Short-term wind power forecasting using ridgelet neural network

3. Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types

4. Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem

5. Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran

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