Electricity price forecasting using Enhanced Probability Neural Network

Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

doi:10.1016/j.enconman.2010.06.006

Cited by 50 publications

(23 citation statements)

References 15 publications

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“…(4). [20] In order to adjust three parameters, which are weights w jk , the center of C and the smoothing parameters σ jk of function φ( • ), Stochastic Gradient Approach (SGA) is adopted. SGA process is: 1) Calculate Euclidean distance ║x ji -c jk ║, 2) Calculate hidden layer output H jk by Eq.…”

Section: Output Layermentioning

confidence: 99%

A Forecasting System of Carbon Price in the Carbon Trading Markets Using Artificial Neural Network

Tsai¹,

Kuo²

2013

IJESD

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Section: Output Layermentioning

confidence: 99%

A Forecasting System of Carbon Price in the Carbon Trading Markets Using Artificial Neural Network

Tsai¹,

Kuo²

2013

IJESD

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“…In [23][24][25], the authors proposed different prediction models using the Artificial Neural Network (ANN). Each proposed model utilized different sets of features created using historical market clearing price, system load and fuel price.…”

Section: Related Workmentioning

confidence: 99%

“…The range of model varies from a simple three-layer architecture to combination models, including the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED). In [25], the author implemented PNN as a classifier, which showed the advantage of a fast learning process, as it requires a single-pass network training stage for adjusting weights. OED was used to find the optimal smoothing parameter, which helps to increase prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Neupane¹,

Woon²,

Aung³

2016

Preprint

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Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

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“…The first group includes the simulation models (Bastian et al, 1999;Deb et al, 2000;Lin et al, 2010) that try to imitate the dispatch, the physical status of power grid, and other system necessities and constraints. These methods model electricity prices within a simulation exercise designed to optimize power flow in a grid with some system constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

Erdoğdu

2016

Energy Economics

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This paper uses high frequency spot price data from fourteen wholesale electricity markets in Europe to analyze asymmetric volatility in European day-ahead power markets with Exponential GARCH (E-GARCH) and TARCH models. Our data set ranges from 1992 to 2015 and consists of approximately 926 thousand observations. As such, this paper constitutes the most extensive and comprehensive work conducted so far on European power markets, to the best of our knowledge. Unlike most of the literature that treats price as a continuous Volatility has decreased in many European countries in post-2008 period. Besides, we find magnitude effect is usually larger than the leverage effect, meaning that the absolute value of price change is relatively more important than the sign of the change (whether it is an increase or a decrease) to explain volatility in European day-ahead power markets.Moreover, the results imply there isn't a uniform inverse leverage effect in electricity prices; that is, price increases are more destabilizing in some European markets (e.g.

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Electricity price forecasting using Enhanced Probability Neural Network

Cited by 50 publications

References 15 publications

A Forecasting System of Carbon Price in the Carbon Trading Markets Using Artificial Neural Network

A Forecasting System of Carbon Price in the Carbon Trading Markets Using Artificial Neural Network

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

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