Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

doi:10.1016/j.enconman.2005.12.008

Cited by 176 publications

(80 citation statements)

References 20 publications

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“…Artificial neural networks and artificial neural networks-fuzzy logic techniques are used in research where short-term load and price forecasting is performed and the results compared. The results obtained are not satisfactory, although the wind pretty good about the forecast results have been obtained [21]. Pandal and others engaged in a similar study on the same topic in terms of working with that they get the results that you achieve better results when compared to observed.…”

Section: Introductionmentioning

confidence: 94%

Energy Management and Smart Grids for Energy Productivity

Yılmaz¹

2015

IJEFM

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Abstract:The first stage to ensure effective energy efficiency is basically to use energy wisely. It is difficult to provide this, for all subscribers, in a network where everyone consumes energy. Therefore, it is necessary to manage this situation. All over the world, the smart grids appear to be the prominent management system about such situations. Effective management of the energy flow can be assured by monitoring and controlling the energy consumption and production plants. One of the most discussed topics in the research is energy efficiency which forms the basis for smart grids. Consumers, production centers and electrical devices in smart grids communicate with each other, and in this way power system can be operated in a more efficient way. Thus, it becomes possible that consumption values can be adapted very successfully to energy production available. In the system established, not only consumer's energy needs is reduced to lower degrees, but also it is ensured that all energy sources (renewable and power plants) are optimized. Consequently, this study provides suggestions to use energy effectively and to manage energy production and consumption by using smart grids in Turkey.

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Section: Introductionmentioning

confidence: 94%

Energy Management and Smart Grids for Energy Productivity

Yılmaz¹

2015

IJEFM

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“…ANN have become popular in various real world applications including prediction and forecasting, function approximation, clustering, speech recognition and synthesis, pattern recognition and classification, and many others. [16] The multi-layer feed forward network will learn to associate the given output vectors with input vectors by adjusting their weights, which are based on the error at the output. The weight modification algorithm is the steepest descent algorithm (often called the delta rule) to minimize a nonlinear function [17].…”

Section: Neural Network Architecturementioning

confidence: 99%

Optimization of workflow scheduling in Utility Management System with hierarchical neural network

Vukmirovic¹,

Erdeljan²,

Lendak³

et al. 2011

IJCIS

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Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.

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“…However, those procedures are either too complex to implement or too simple to have enough accuracy [3]. Artificial neural network (ANN) technique is a simple and powerful tool for forecasting [4]- [7]. Hybrid intelligent methods are complex to implement and also too powerful tool for forecasting [8]- [15].…”

Section: Introductionmentioning

confidence: 99%

An Elm for Bi-Classification of Vertically Bundled Electricity Market Prices

Anbazhagan

Sivakumar²

2017

IJSC

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Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM) to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs) are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.

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Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

Cited by 176 publications

References 20 publications

Energy Management and Smart Grids for Energy Productivity

Energy Management and Smart Grids for Energy Productivity

Optimization of workflow scheduling in Utility Management System with hierarchical neural network

An Elm for Bi-Classification of Vertically Bundled Electricity Market Prices

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