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Article
Publication date: 5 June 2019

Samrad Jafarian-Namin, Alireza Goli, Mojtaba Qolipour, Ali Mostafaeipour and Amir-Mohammad Golmohammadi

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

Abstract

Purpose

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

Design/methodology/approach

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

Findings

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Originality/value

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

Details

International Journal of Energy Sector Management, vol. 13 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

Article
Publication date: 30 September 2020

Ali Mostafaeipour, Mojtaba Qolipour, Mostafa Rezaei, Mehdi Jahangiri, Alireza Goli and Ahmad Sedaghat

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from…

Abstract

Purpose

Every day, the sun provides by far more energy than the amount necessary to meet the whole world’s energy demand. Solar energy, unlike fossil fuels, does not suffer from depleting resource and also releases no greenhouse gas emissions when being used. Hence, using solar irradiance to produce electricity via photovoltaic (PV) systems has significant benefits which can lead to a sustainable and clean future. In this regard, the purpose of this study is first to assess the technical and economic viability of solar power generation sites in the capitals of the states of Canada. Then, a novel integrated technique is developed to prioritize all the alternatives.

Design/methodology/approach

In this study, ten provinces in Canada are evaluated for the construction of solar power plants. The new hybrid approach composed of data envelopment analysis (DEA), balanced scorecard (BSC) and game theory (GT) is implemented to rank the nominated locations from techno-economic-environmental efficiency aspects. The input data are obtained using HOMER software.

Findings

Applying the proposed hybrid approach, the order of high to low efficiency locations was found as Winnipeg, Victoria, Edmonton, Quebec, Halifax, St John’s, Ottawa, Regina, Charlottetown and Toronto. Construction of ten solar plants in the ten studied locations was assessed and it was ascertained that usage of solar energy in Winnipeg, Victoria and Edmonton would be economically and environmentally justified.

Originality/value

As to novelty, it should be clarified that the authors propose an effective hybrid method combining DEA, BSC and GT for prioritizing all available scenarios concerned with the construction of a solar power plant.

Details

Journal of Engineering, Design and Technology , vol. 19 no. 3
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 13 May 2022

Alireza Bakhshi, Amir Aghsami and Masoud Rabbani

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief…

63

Abstract

Purpose

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.

Design/methodology/approach

This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.

Findings

This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.

Originality/value

Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

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