Special Issue "Forecasting Models of Electricity Prices"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 August 2016).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Javier Contreras
Website
Guest Editor
Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla—La Mancha, Campus Universitario S/N, 13071 Ciudad Real, Spain
Interests: power system operation; power system planning; distributed generation; distribution planning; renewable energy sources; smart grid; distribution reliability; demand-side management; energy storage; electric vehicles; optimization
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Special Issue Information

Dear Colleagues,

The electric power industry has been in transition, from a centralized, towards a deregulated, production scheme since the early 1980s. Previous centralized schemes were based on electricity tariffs that were paid by the customers as a function of the aggregate cost of production. In the new unbundled scheme, price forecasting has become an important tool for electric companies and customers to decide on their production offers and demand bids and for regulators to characterize the degree of competition of the market.

Electricity prices have unique features that are not observed in other markets, such as weekly and daily seasonalities, on-peak vs. off-peak hours, price spikes, etc. The fact that electricity is not easily storable and the requirement of meeting the demand at all times makes the development of forecasting techniques a challenging issue.

This Special Issue will include the most important forecasting techniques applied to the forecasting of electricity prices, such as:

  • Statistical time series models: auto regression models, GARCH,
  • Fourier and wavelet transform models
  • Fundamental or structural econometric models
  • Regime-switching models: Markov, jump diffusion,
  • Multi-agent and game theoretic equilibrium models: Nash-Cournot, supply function equilibrium, agent-based methods, etc.
  • Artificial intelligence models: Neural networks, fuzzy logic, support vector machines, etc.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of electricity price forecasting.

Prof. Javier Contreras
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Electricity price forecasting
  • Time series
  • Transform models
  • Fundamental models
  • Regime-switching
  • Multi-agent
  • Market equilibrium
  • Artificial intelligence

Published Papers (12 papers)

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Editorial

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Open AccessEditorial
Forecasting Models of Electricity Prices
Energies 2017, 10(2), 160; https://doi.org/10.3390/en10020160 - 29 Jan 2017
Cited by 1
Abstract
This book contains the successful invited submissions [1,2,3,4,5,6,7,8,9,10,11] to a Special Issue of Energies on the subject area of “Forecasting Models of Electricity Prices”. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available

Research

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Open AccessArticle
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
Energies 2017, 10(1), 77; https://doi.org/10.3390/en10010077 - 10 Jan 2017
Cited by 14
Abstract
Forecasting of electricity prices is important in deregulated electricity markets for all of 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 [...] Read more.
Forecasting of electricity prices is important in deregulated electricity markets for all of 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 the 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 participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the 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 extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming
Energies 2016, 9(12), 1069; https://doi.org/10.3390/en9121069 - 16 Dec 2016
Cited by 14
Abstract
Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, [...] Read more.
Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network
Energies 2016, 9(12), 987; https://doi.org/10.3390/en9120987 - 25 Nov 2016
Cited by 3
Abstract
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, [...] Read more.
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various RBFNNs. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Parametric Density Recalibration of a Fundamental Market Model to Forecast Electricity Prices
Energies 2016, 9(11), 959; https://doi.org/10.3390/en9110959 - 17 Nov 2016
Cited by 10
Abstract
This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is [...] Read more.
This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The recalibration methodology is innovative in seeking to perform the recalibration into parametrically defined density functions. The density estimation method selects from a wide diversity of general four-parameter distributions to fit hourly spot prices, in which the first four moments are dynamically estimated as latent functions of the outputs from the fundamental model and several other plausible exogenous drivers. The proposed approach demonstrated its effectiveness against benchmark methods across the full range of percentiles of the price distribution and performed particularly well in the tails. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market
Energies 2016, 9(10), 804; https://doi.org/10.3390/en9100804 - 08 Oct 2016
Cited by 10
Abstract
For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the [...] Read more.
For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market
Energies 2016, 9(9), 721; https://doi.org/10.3390/en9090721 - 07 Sep 2016
Cited by 24
Abstract
This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of [...] Read more.
This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessEditor’s ChoiceArticle
Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term
Energies 2016, 9(9), 693; https://doi.org/10.3390/en9090693 - 31 Aug 2016
Cited by 12
Abstract
The uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary [...] Read more.
The uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting
Energies 2016, 9(8), 621; https://doi.org/10.3390/en9080621 - 05 Aug 2016
Cited by 36
Abstract
In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge [...] Read more.
In day-ahead electricity price forecasting (EPF) variable selection is a crucial issue. Conducting an empirical study involving state-of-the-art parsimonious expert models as benchmarks, datasets from three major power markets and five classes of automated selection and shrinkage procedures (single-step elimination, stepwise regression, ridge regression, lasso and elastic nets), we show that using the latter two classes can bring significant accuracy gains compared to commonly-used EPF models. In particular, one of the elastic nets, a class that has not been considered in EPF before, stands out as the best performing model overall. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection
Energies 2016, 9(8), 618; https://doi.org/10.3390/en9080618 - 04 Aug 2016
Cited by 10
Abstract
The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers [...] Read more.
The day-ahead electricity market is closely related to other commodity markets such as the fuel and emission markets and is increasingly playing a significant role in human life. Thus, in the electricity markets, accurate electricity price forecasting plays significant role for power producers and consumers. Although many studies developing and proposing highly accurate forecasting models exist in the literature, there have been few investigations on improving the forecasting effectiveness of electricity price from the perspective of reducing the volatility of data with satisfactory accuracy. Based on reducing the volatility of the electricity price and the forecasting nature of the radial basis function network (RBFN), this paper successfully develops a two-stage model to forecast the day-ahead electricity price, of which the first stage is particle swarm optimization (PSO)-core mapping (CM) with self-organizing-map and fuzzy set (PCMwSF), and the second stage is selection rule (SR). The PCMwSF stage applies CM, fuzzy set and optimized weights to obtain the future price, and the SR stage is inspired by the forecasting nature of RBFN and effectively selects the best forecast during the test period. The proposed model, i.e., CM-PCMwSF-SR, not only overcomes the difficulty of reducing the high volatility of the electricity price but also leads to a superior forecasting effectiveness than benchmarks. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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Open AccessArticle
Market Equilibrium and Impact of Market Mechanism Parameters on the Electricity Price in Yunnan’s Electricity Market
Energies 2016, 9(6), 463; https://doi.org/10.3390/en9060463 - 17 Jun 2016
Cited by 6
Abstract
In this paper, a two-dimensional Cournot model is proposed to study generation companies’ (GENCO’s) strategic quantity-setting behaviors in the newly established Yunnan’s electricity market. A hybrid pricing mechanism is introduced to Yunnan’s electricity market with the aim to stimulate electricity demand. Market equilibrium [...] Read more.
In this paper, a two-dimensional Cournot model is proposed to study generation companies’ (GENCO’s) strategic quantity-setting behaviors in the newly established Yunnan’s electricity market. A hybrid pricing mechanism is introduced to Yunnan’s electricity market with the aim to stimulate electricity demand. Market equilibrium is obtained by iteratively solving each GENCO’s profit maximization problem and finding their optimal bidding outputs. As the market mechanism is a key element of the electricity market, impacts of different market mechanism parameters on electricity price and power generation in market equilibrium state should be fully assessed. Therefore, based on the proposed model, we precisely explore the impacts on market equilibrium of varying parameters such as the number of GENCOs, the quantity of ex-ante obligatory-use electricity contracts (EOECs) and the elasticity of demand. Numerical analysis results of Yunnan’s electricity market show that these parameters have notable but different effects on electricity price. A larger number of GENCOs or less EOEC contracted with GENCOs will have positive effects on reducing the price. With the increase of demand elasticity, the price falls first and then rises. Comparison of different mechanisms and relationship between different parameters are also analyzed. These results should be of practical interest to market participants or market designers in Yunnan’s or other similar markets. Full article
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Open AccessArticle
Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case
Energies 2016, 9(3), 193; https://doi.org/10.3390/en9030193 - 15 Mar 2016
Cited by 15
Abstract
One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel [...] Read more.
One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices) Printed Edition available
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