Long-term load forecasting of Iranian power grid using fuzzy and artificial neural networks

Dalvand, Mehran; Azami, Sajjad; Tarimoradi, Hadi

doi:10.1109/upec.2008.4651538

Cited by 21 publications

(14 citation statements)

References 11 publications

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“…A number of affecting factors has been discussed in [5,6] which may be responsible for uncertainty in long term forecast. Due to these uncontrollable factors long term forecast is inaccurate.…”

Section: A Parametric or Conventional Methodsmentioning

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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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: A Parametric or Conventional Methodsmentioning

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)

View full text Add to dashboard Cite

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“…[28] Before that, Kermanshahi [91] in 1998 used ANN forecast load for 10 years, Ekonomou [92] used ANN to forecast load in Greece. Other commendable work in LTLF using ANN is reported in literatures [93][94][95][96][97][98][99][100].…”

Section: Long-term Load Forecasting Overviewmentioning

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

135

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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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“…Another study, which only considers demand, also applied the method based on particle swarm optimization. Khoa et al proposed 3 models (ANN, wavelet network, and functional link net), and Dalvand et al endorsed a model of fuzzy and ANN. Both projects use GDP variables and consumer and tariff indexes.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Int Trans Electr Energ Syst

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Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

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Long-term load forecasting of Iranian power grid using fuzzy and artificial neural networks

Cited by 21 publications

References 11 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

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

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