Electricity estimation using genetic algorithm approach: a case study of Turkey

Öztürk, Harun Kemal; Ceylan, Halim; Canyurt, Olcay Ersel; Hepbaşlı, Arif

doi:10.1016/j.energy.2004.08.008

Cited by 133 publications

(61 citation statements)

References 8 publications

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“…separately, which is not only essential for policy formulation in Turkey but also will make more detailed and accurate understanding of the trends possible. Ozturk et al (2005) conclude that official total electricity demand projection for the period of 1996-2001 overestimated demand by 36% either due to inappropriateness of the model used or in order to justify the construction of new electric power plants to use excess amount of natural gas. In line with this conclusion; in this study, we find that the official net electricity consumption projection for 2014 again overestimates demand at least by 34% compared to the forecasted values based on ARIMA modelling.…”

Section: Evaluation Of Study Resultsmentioning

confidence: 99%

Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

2007

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In the early 2000s, the Republic of Turkey has initiated an ambitious reform program in her electricity market, which requires privatization, liberalization as well as a radical restructuring. The most controversial reason behind, or justification for, recent reforms has been the rapid electricity demand growth; that is to say, the whole reform process has been a part of the endeavors to avoid so-called "energy crisis". Using cointegration analysis and ARIMA modeling, the present article focuses on this issue by both providing an electricity demand estimation and forecast, and comparing the results with official projections. The study concludes, first, that consumers' respond to price and income changes is quite limited and therefore there is a need for * Corresponding author. Tel.: +90-312-2872560 E-mail: erkan@erdogdu.net URL: http://erkan.erdogdu.net/english b The author is working as an Energy Expert in Energy Market Regulatory Authority of the Republic of Turkey. The present article is a part of the dissertation submitted by the author in fulfillment of the requirements for the award of the degree "MSc in Energy Economics and Policy". In October 2005, the author is awarded an "MSc with distinction" by the Department of Economics, University of Surrey (UK). The views, findings and conclusions expressed in this article are entirely those of the author and do not represent in any way the views of any institution he is affiliated with. 2 economic regulation in Turkish electricity market; and second, that the current official electricity demand projections highly overestimate the electricity demand, which may endanger the development of both a coherent energy policy in general and a healthy electricity market in particular.

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Section: Evaluation Of Study Resultsmentioning

confidence: 99%

Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

2007

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“…The GAs forecasting approach has been applied to a wide range of disciplines in recent times, including electric energy estimation (Ozturk et al 2005), energy demand prediction (Ghanbari et al 2013), housing price forecasting (Jirong et al 2011), tourism demand forecasting (Hernández-López and Cáceres-Hernández 2007; Hong et al 2011), traffic accident severity prediction (Akgüngör and Doğan 2009;Kunt et al 2011), and transport energy demand prediction (Haldenbilen and Ceylan 2005). Despite the reported benefits of GAs, there has only been one reported study that has applied GAs in the aviation industry.…”

Section: Traditional Air Travel Demand Forecasting Approachesmentioning

confidence: 99%

“…The basic operations of GAs include selection, a crossover of genetic information between reproducing parents and a mutation of genetic information which affects the binary strings characteristic in natural evolution (Ozturk et al 2005). If GAs are suitably encoded, then they can be used to solve real-world problems by mimicking this process (Akgüngör and Doğan 2009).…”

Section: Mutationmentioning

confidence: 99%

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

Srisaeng

Baxter

Richardson³

et al. 2015

J.Aerosp. Technol. Manag.

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This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia's domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively.

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“…are used for electrical load forecasting. Many researchers have studied on forecasting of Turkey's electricity energy demand and peak load using different methods [7][8][9][10][11][12]. In these studies, in particular neural networks (NN), genetic algorithms (GA) and MM are used.…”

Section: Introductionmentioning

confidence: 99%

“…The medium-term and LTLF take into account the historical load, weather, the number of customers in different categories and other factors [4]. Many LTLF techniques have been proposed used for resource planning and utility expansion in the last 30 years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Many software packages have been made for safety and quality of energy systems management.…”

Section: Introductionmentioning

confidence: 99%

Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting

Çetinkaya¹

2017

ijisae

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Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error (MAE) are used. The obtained results show that the proposed algorithms are available.

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Electricity estimation using genetic algorithm approach: a case study of Turkey

Cited by 133 publications

References 8 publications

Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey

A Forecasting Tool for Predicting Australia’s Domestic Airline Passenger Demand Using a Genetic Algorithm

Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting

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