Medium term system load forecasting with a dynamic artificial neural network model

Ghiassi, M.; Zimbra, David; Saidane, Hassine

doi:10.1016/j.epsr.2005.06.010

Cited by 153 publications

(85 citation statements)

References 30 publications

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“…Ref. [30] proposed a dynamic ANN, called DAN2, which is based on the principle of self-learning at each layer till the desired network performance criteria is reached, and thereby deciding the architecture of neural network.…”

Section: Definition References For Mtlf and Ltlf Are Elaborated In Dmentioning

confidence: 99%

“…In this time-frame, MTLF also contributes towards allocation of available resources and development of other infrastructure elements that is feasible during mid-term horizon [62]. An example is improving the congestion management in transmission grids, thereby improving overall system efficiency and cost of energy for consumer [30]. Added advantage with accurate MTLF is that deregulated firms can utilise the required information to guide the improvement of their transmission grid as well as distribution system.…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

“…A dynamic ANN-based MTLF model (DAN2) proposed by [30] compared their model with statistical approach (ARIMA), and concluded that the forecasted values were more accurate as measured by MAPE…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

“…[77,[79][80][81]. AI-based methods have been widely used for MTLF, mostly ANN [30,32,71,78,[82][83][84].…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

See 3 more Smart Citations

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

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

134

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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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Section: Definition References For Mtlf and Ltlf Are Elaborated In Dmentioning

confidence: 99%

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

“…[77,[79][80][81]. AI-based methods have been widely used for MTLF, mostly ANN [30,32,71,78,[82][83][84].…”

Section: Mid-term Load Forecasting Overviewmentioning

confidence: 99%

See 2 more Smart Citations

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

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

134

View full text Add to dashboard Cite

show abstract

“…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%

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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Medium-term retailer's planning and participation strategy considering electricity market uncertainties

Kharrati

Kazemi

Ehsan

2015

Int. Trans. Electr. Energ. Syst.

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Summary This paper presents a risk‐constrained programming approach to solve a retailer's medium‐term planning problem. A retailer tries to maximize its profit via determining the optimal price offered to the customers as well as optimal strategy of participating in futures and pool markets. The uncertainty of pool prices is modeled by an envelope‐bound information‐gap model. Another source of uncertainty in this problem is the clients' demand, which is considered via a scenario generation method. The proposed method is formulated as a bi‐level stochastic programming problem based on the information‐gap decision theory. The Karush–Kuhn–Tucker optimality conditions are used to convert the bi‐level problem into a single‐level robust optimization problem. The performance of the proposed method is demonstrated using a case study of the New England market, and results are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

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Medium term system load forecasting with a dynamic artificial neural network model

Cited by 153 publications

References 30 publications

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

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

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

Medium-term retailer's planning and participation strategy considering electricity market uncertainties

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