A neural network architecture for load forecasting

Despite increasing applications of artificial neural networks (NNs) to forecasting over the past decade, opinions regarding their contribution are mixed. Evaluating research in this area has been difficult, due to lack of clear criteria. We identified eleven guidelines that could be used in evaluating this literature. Using these, we examined applications of NNs to business forecasting and prediction. We located 48 studies done between 1988 and 1994. For each, we evaluated how effectively the proposed technique was compared with alternatives (effectiveness of validation) and how well the technique was implemented (effectiveness of implementation). We found that eleven of the studies were both effectively validated and implemented. Another eleven studies were effectively validated and produced positive results, even though there were some problems with respect to the quality of their NN implementations. Of these 22 studies, 18 supported the potential of NNs for forecasting and prediction. Copyright © 1998 John Wiley & Sons, Ltd.

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“…Hsu et al (1993) Various NN learning algorithms . Bacha and Meyer (1992) Con®gurations . Peng et al (1993) Box±Jenkins .…”

Section: Coats and Fantmentioning

confidence: 99%

How effective are neural networks at forecasting and prediction? A review and evaluation

1998

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“…The hourly-partitioning scheme, obtained by forming a model for each separated hour and the day partitioning models are compared in [31,37], preferring the latter to the first. In [38] the authors propose 24 tightly coupled sub neural networks, one for each hour of a day, as in Fig 2 (e). A season-partitioning scheme is obtained forming a model for each separated season.…”

Section: 2mentioning

confidence: 99%

A Review of Connectionist Models for Short Term Electrical Load Forecasting

Piras¹,

Czernichow²,

Imhof³

et al. 1998

Industrial Applications of Neural Networks

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Mastering the production, transmission and distribution of electrical energy is a challenge to which the electrical engineer is perpetually confronted with. A precise Short Term electrical Load Forecasting (STLF) results in economic cost savings and increased security operation's conditions, allowing electrical companies to commit their own production resources in order to optimize energy prices and exchanges with vendors and clients. In this review, we expose a classification in six sub-groups of Artificial Neural Networks based models used for short term electrical load forecasting, proposed in the last four years. 143Industrial Applications of Neural Networks Downloaded from www.worldscientific.com by UNIVERSITY OF BIRMINGHAM LIBRARY -INFORMATION SERVICES on 03/21/15. For personal use only. Industrial Applications of Neural Networks Downloaded from www.worldscientific.com by UNIVERSITY OF BIRMINGHAM LIBRARY -INFORMATION SERVICES on 03/21/15. For personal use only.

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“…In the past few years, many papers report the use of ANNs in short-term electricity demand forecasting [3], [4], [5], [6]. Most recently, some papers have repported the use of ANNs to forecast next-day electricity prices [7], [8].…”

Section: Introductionmentioning

confidence: 98%

Forecasting electricity prices in spot markets — One week horizon approach

Duarte

Fidalgo

Saraiva

2009

2009 IEEE Bucharest PowerTech

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This paper describes the methodology developed to build estimates of electricity prices having the horizon of one week. This approach uses artificial neural networks and includes a particular treatment of weekends and national holidays as a way to improve the quality of the results. The developed approach was tested using data obtained from the Spanish market operator for the time period of 2006 to 2008. The obtained value of MAPEMean Absolute Percentage Error -was 12,62% for workdays and 10,73% for holidays and weekends. The obtained results show that this study has interest to the market agents in question, since realistic forecasting was achieved.

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A neural network architecture for load forecasting

Cited by 21 publications

References 8 publications

How effective are neural networks at forecasting and prediction? A review and evaluation

How effective are neural networks at forecasting and prediction? A review and evaluation

A Review of Connectionist Models for Short Term Electrical Load Forecasting

Forecasting electricity prices in spot markets — One week horizon approach

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A neural network architecture for load forecasting

Cited by 21 publications

References 8 publications

How effective are neural networks at forecasting and prediction? A review and evaluation

How effective are neural networks at forecasting and prediction? A review and evaluation

A Review of Connectionist Models for Short Term Electrical Load Forecasting

Forecasting electricity prices in spot markets &#x2014; One week horizon approach

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Forecasting electricity prices in spot markets — One week horizon approach