Long Term Energy Consumption Forecasting Using Genetic Programming

Karabulut, Korhan; Alkan, Ahmet; Yılmaz, Ahmet Serdar

doi:10.3390/mca13020071

Cited by 30 publications

(18 citation statements)

References 15 publications

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“…GA works on the survival of best fitness which combines the process of natural selection and natural genetics. GAs is found to be promising robust and suitable for load forecasting [21,22]. 4) Support Vector Machine: SVM [47,48] is a powerful technique used for data classification and regression.…”

Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

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Analysis and comparison of various methods available for load forecasting: An overview

Khatoon

Ibraheem

Singh

et al. 2014

2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls With Their Impact on Humanity (CIPECH)

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Accurate load forecasting is essential for power system planning and operation. As the electricity end uses expand, load forecasting technique becomes more complicated. Depending on the nature of load variations, one particular method may not be suitable in each case. Before choosing a particular method, an overview of different techniques is required. This paper presents the analysis of various techniques for load forecasting. A comparative analysis is presented to understand the performance of each technique. None of them is found to be perfect alone; the trend is towards hybrid models.Keywords-load forecasting, short term load forecast, long term load forecast.I.

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Section: B Artificial Intelligence Based Methodsmentioning

confidence: 99%

“…1) Genetic Algorithm: The genetic algorithm (GA) approach [21,22] is very effective optimization algorithm and capable to obtain global minima and maxima. Therefore best suitable for calculating minimum and maximum load leading to improved accuracy of load forecasting model.…”

Section: Soft Computing Based Forecasting Techniquesmentioning

confidence: 99%

Analysis and comparison of various methods available for load forecasting: An overview

Khatoon

Ibraheem

Singh

et al. 2014

2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls With Their Impact on Humanity (CIPECH)

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“…Examples are genetic algorithm [54][55][56], expert system [57][58][59][60], and evolutionary computation algorithms [61].…”

Section:  Other Hybrid Methodsmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

127

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…Akay and Atak (2007) suggested a grey prediction with rolling mechanism algorithm for electricity demand forecasting. Karabulut et al (2008) showed the application of genetic programming to local load distribution forecasting in the Kahramanmaras region of Turkey. Çunkaş and Altun (2010) presented an approach based on economic indicators for Turkey's long-term electricity demand forecasting from 2008 to 2014 by using ANN.…”

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confidence: 99%

Turkey's Electricity Consumption Forecasting Using Genetic Programming

Çunkaş

Taşkıran

2011

Energy Sources, Part B: Economics, Planning, and Policy

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Turkey's energy demand has been increasing rapidly as a result of rapid urbanization and industrialization. The energy investment requirement will be US$130 billion by the year 2020. Electricity energy has a vital importance among the energy sector. In this study the current state of the electricity energy production and consumption of Turkey is investigated and the electricity energy consumption is forecasted by using genetic algorithm. The obtained results are compared with conventional regression analyses techniques, and the estimated values of the Ministry of Energy and Natural Resources. The electricity demand in the year 2020 is estimated to be 315.02 billion kWh compared to the 189.52 billion kWh needed in the year 2007.Energy has become an important topic in the last couple of years and is directly related to the development of a country and the living standards of its people. Turkey is currently in a rapid industrialization process with a young and dynamic population of over 70 million (Tunç et al., 2006). Turkey has been quickly growing in terms of both its economy and its population and the Turkish economy has undergone a transformation from the agricultural to the industrial sector. In parallel, its demand for energy, particularly for electricity, has been rising rapidly. Electricity is an essential source of energy for the technical, social, and economic development of Turkey as in other countries (Hamzaçebi, 2007). Electricity demand forecasting is extremely important for not only aiming at costefficient investments in capacity expansion planning, but also plays an effective role for energy suppliers, financial institutions, and other participants in electric energy generation, transmission, distribution, and markets (Akay and Atak, 2007).Since 1984, the Ministry of Energy and Natural Resources (MENR) has carried out projections for Turkey's energy demands by using the Model for Analysis of Energy Demand (MAED) simulation technique. In the model, energy demand forecasting is realized by considering past situations of the country's economic, demographic, and social structure. Yet, the MEAD model is observed to be unsuccessful compared to the techniques based on observatory techniques (i.e., meta heuristic techniques; Ediger and Tatlidil, 2002).

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Long Term Energy Consumption Forecasting Using Genetic Programming

Cited by 30 publications

References 15 publications

Analysis and comparison of various methods available for load forecasting: An overview

Analysis and comparison of various methods available for load forecasting: An overview

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Turkey's Electricity Consumption Forecasting Using Genetic Programming

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