Cascaded artificial neural networks for short-term load forecasting

Al-Fuhaid, A.S.; El‐Sayed, Mostafa A.; Mahmoud, Magdi S.

doi:10.1109/59.627852

Cited by 114 publications

(35 citation statements)

References 9 publications

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“…A similar case is presented in [44], where 30-minute predictions are provided 24 h in advance, but using more than 50 input variables. These works are prone to the curse of dimensionality effect as reported in [54]: the number of training patterns required to properly train the network increases exponentially with the dimension of the input space.…”

Section: Introductionmentioning

confidence: 99%

“…References [35][36][37][38][39] and [49,50] offer generally short prediction horizons, normally forecasting values in the next hour. While this work employs 29 input variables and 16 neurons in the hidden layer, [44,[51][52][53] use high dimensional input spaces (with a number of input variables ranging from 40 and 50 and neurons in the hidden layer between 24 and 50) and therefore require a bigger training database to reach similar results.…”

Section: Comparison With Other Solutionsmentioning

confidence: 99%

“…While the solutions studied in the literature [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][55][56][57][58][59] present sometimes good prediction efficiency figures (normally their MAPEs are around 2%), they deal almost exclusively with big areas, and mainly entire countries, and they are never applied to smaller environments of the size of small cities or microgrids. Therefore, they do not give any evidence of how will they behave when applied to highly variable load curves.…”

Section: Geographical Area In Load Forecastingmentioning

confidence: 99%

“…Alfuhaid et al [44] use a small ANN that pre-processes a data set and produces peak, valley and total load forecasts; these forecasts, in combination with other data, are used as input to a larger ANN to obtain next-day load forecast. Lamedica et al [45] present 12 ANNs-one for each month of the year-where load curves are classified using Kohonen's Self-Organized Map.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

et al. 2013

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Received : 28 November 2012; in revised form: 18 February 2013 / Accepted: 20 February 2013 / Published: 5 March 2013 Abstract: Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.

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Section: Introductionmentioning

confidence: 99%

Section: Comparison With Other Solutionsmentioning

confidence: 99%

Section: Geographical Area In Load Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

et al. 2013

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“…However, These techniques have limited accuracy because they ignore important weather effects, are time consuming, require extensive user intervention and may become numerically unstable [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling and forecasting electric daily peak loads using abductive networks

Abdel-Aal

2006

International Journal of Electrical Power & Energy Systems

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Modelling of residential energy consumption at the national level

Aydinalp

Ugursal

Fung

2003

Int. J. Energy Res.

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SUMMARYThree methods are currently used to model residential energy consumption at the national level: the engineering method (EM), the conditional demand analysis (CDA) method, and the neural network (NN) method. While the use of the first two methods has been established over the past decade for residential energy modelling, the use of NN method is still in the development and verification phase.The EM involves developing a housing database representative of the national housing stock and estimating the energy consumption of the dwellings in the database using a building energy simulation program. CDA is a regression-based method in which the regression attributes consumption to end-uses on the basis of the total household energy consumption. The NN method models the residential energy consumption as a neural network, which is an information-processing model inspired by the way the densely interconnected, parallel structure of the brain processes information.In this paper, the three methods are briefly described and a comparative assessment of the three methods is presented.

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Cascaded artificial neural networks for short-term load forecasting

Cited by 114 publications

References 9 publications

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Modeling and forecasting electric daily peak loads using abductive networks

Modelling of residential energy consumption at the national level

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