Short term and medium term power distribution load forecasting by neural networks

Yalçınöz, Tankut; Eminoğlu, U.

doi:10.1016/j.enconman.2004.07.005

Cited by 138 publications

(57 citation statements)

References 14 publications

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“…,Many groups of researchers used this forecasting approach for electricity mid term load forecasting. Also this method can be used for electricity peak load forecasting of distribution system [37]. By using the relationship of learning data in the past, present and the future of temperature, the network can forecast a daily peak load, total load of day and electricity monthly load consumption.…”

Section: B Artificial Intelligence Technology Methods and Other Methmentioning

confidence: 99%

“…2) Artificial Neural Network (ANN) [34][35][36][37][38] used Artificial Neural Network (ANN) approach to forecast electrical demand load, by using the data supporting from the government. The forecasting can be performed the results in yearly (to 15 years), weekly (to 3 years) and hourly (to 24 hours).…”

Section: B Artificial Intelligence Technology Methods and Other Methmentioning

confidence: 99%

“…Historical load inputs and meteorological data such as monthly maximum temperature, minimum temperature are used in [35][36][37] and [39,40]. Economic variables are also included in [38].…”

Section: A Data Inputs 1) I Nput Classificationmentioning

confidence: 99%

See 2 more Smart Citations

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

Bunnoon¹,

Chalermyanont²,

Limsakul³

2010

IJET

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Abstract-This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).

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Section: B Artificial Intelligence Technology Methods and Other Methmentioning

confidence: 99%

Section: B Artificial Intelligence Technology Methods and Other Methmentioning

confidence: 99%

See 1 more Smart Citation

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

Bunnoon¹,

Chalermyanont²,

Limsakul³

2010

IJET

View full text Add to dashboard Cite

show abstract

“…Refs. [68][69][70] modelled MTLF in monthly forecast step, and refs. [21,[71][72] considered a horizon of 12 months or 1 year for their study.…”

Section: Mid-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

122

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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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“…It can be also applied to energy forecasting problems. Some of these applications are short and medium term load forecasting [38,3], adaptive price forecasting [18], forecasting transport energy demand [42].Also in some cases ANN can give us a better output if it is trained with the preprocessed data [1,2] Iran, 1982. conducted a study by the name of "energy demand estimation".…”

Section: Introductionmentioning

confidence: 99%

Integration of multi layer perceptron and design of experiments for forecasting household electricity consumption

Azadeh¹,

Faiz²

2007

REPQJ

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Due to various seasonal and monthly changes in electricity consumption, it is difficult to model it with conventional methods. This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for household electricity consumption forecasting. This is the first study which uses MLP for forecasting household electricity consumption. Previous studies base their verification by the difference in error estimation. However this study shows the advantage of MLP methodology through design of experiment (DOE). Moreover, DOE is based on analysis of variance (ANOVA) and Duncan Multiple Range Test (DMLT). Furthermore, actual data is compared with ANN-MLP and conventional regression model. The significance of this study is integration of MLP and DOE for improved processing, development and testing of household electricity consumption. Moreover, it would provide more reliable and precise forecasting for policy makers. To show the applicability and superiority of the integrated approach, annual household electricity consumption in Iran from 1974 to 2003 was collected for processing, training and testing purpose.

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Short term and medium term power distribution load forecasting by neural networks

Cited by 138 publications

References 14 publications

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

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

Integration of multi layer perceptron and design of experiments for forecasting household electricity consumption

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