Forecasting Electricity Prices: A Machine Learning Approach

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

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A linear regression pattern for electricity price forecasting in the Iberian electricity market

Revista Facultad de Ingeniería Universidad de Antioquia ◽

10.17533/udea.redin.20190522 ◽

2019 ◽

pp. 117-127

Author(s):

Ângela Paula Ferreira ◽

Jenice Gonçalves Ramos ◽

Paula Odete Fernandes

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Linear Regression Analysis ◽

Multiple Linear Regression Model ◽

Electricity Price ◽

Price Forecasting ◽

Electricity Prices ◽

Market Participants ◽

Electricity Price Forecasting ◽

The Impact

The Iberian Market for Electricity resulted from a cooperation process developed by the Portuguese and Spanish administrations, aiming to promote the integration of the electrical systems of both countries. This common market consists of organized markets or power exchanges, and non-organised markets where bilateral over-the-counter trading takes place with or without brokers. Within this scenario, electricity price forecasts have become fundamental to the process of decision-making and strategy development by market participants. The unique characteristics of electricity prices such as non-stationarity, non-linearity and high volatility make this task very difficult. For this reason, instead of a simple time forecast, market participants are more interested in a causal forecast that is essential to estimate the uncertainty involved in the price. This work focuses on modelling the impact of various explanatory variables on the electricity price through a multiple linear regression analysis. The quality of the estimated models obtained validates the use of statistical or causal methods, such as the Multiple Linear Regression Model, as a plausible strategy to achieve causal forecasts of electricity prices in medium and long-term electricity price forecasting. From the evaluation of the electricity price forecasting for Portugal and Spain, in the year of 2017, the mean absolute percentage errors (MAPE) were 9.02% and 12.02%, respectively. In 2018, the MAPE, evaluated for 9 months, for Portugal and Spain equals 7.12% and 6.45%, respectively.

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ESTIMATING METHODS OF INVESTMENTS EFFICIENCY TO OWN GENERATION UNDER THE RISK CONDITIONS

Strategic decisions and risk management ◽

10.17747/2078-8886-2016-2-78-84 ◽

2016 ◽

pp. 78-84 ◽

Cited By ~ 1

Author(s):

O. V. Klimovets ◽

V. A. Zubakin

Keyword(s):

Risk Assessment ◽

Economic Efficiency ◽

Quantitative Methods ◽

Cash Flows ◽

Energy Resources ◽

Future Price ◽

Energy Prices ◽

Electricity Prices ◽

Investment Projects

The article is devoted to the assessment of the on-site generation effectiveness taking into account the risk associated with the uncertainty of future values of energy prices. It is shown that the economic efficiency is significantly affected by the unevenness of growth in energy prices and the correlation between load profile and graph of wholesale electricity prices.Restrictions on the applying existing approaches to evaluating the effectiveness of investment projects, based on calculation of uniquely defined cash flows, are approved. The paper shows the necessity of taking risks into account in order to increase the quality of decisions given the influence of energy resources prices on project’s economic efficiency and the uncertainty of future price values. Based on the analysis of quantitative methods of risk assessment it is proposed to use fuzzy-set approach as one of the most effective methods in the conditions of uncertainty of future values.

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Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques

10.32920/ryerson.14649129.v1 ◽

2021 ◽

Author(s):

Harmanjot Singh Sandhu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Electricity Markets ◽

Electricity Market ◽

Numerical Experiments ◽

Status Report ◽

Electricity Prices ◽

Forecasting Accuracy ◽

Using Data ◽

The Impact

Various machine learning-based methods and techniques are developed for forecasting day-ahead electricity prices and spikes in deregulated electricity markets. The wholesale electricity market in the Province of Ontario, Canada, which is one of the most volatile electricity markets in the world, is utilized as the case market to test and apply the methods developed. Factors affecting electricity prices and spikes are identified by using literature review, correlation tests, and data mining techniques. Forecasted prices can be utilized by market participants in deregulated electricity markets, including generators, consumers, and market operators. A novel methodology is developed to forecast day-ahead electricity prices and spikes. Prices are predicted by a neural network called the base model first and the forecasted prices are classified into the normal and spike prices using a threshold calculated from the previous year’s prices. The base model is trained using information from similar days and similar price days for a selected number of training days. The spike prices are re-forecasted by another neural network. Three spike forecasting neural networks are created to test the impact of input features. The overall forecasting is obtained by combining the results from the base model and a spike forecaster. Extensive numerical experiments are carried out using data from the Ontario electricity market, showing significant improvements in the forecasting accuracy in terms of various error measures. The performance of the methodology developed is further enhanced by improving the base model and one of the spike forecasters. The base model is improved by using multi-set canonical correlation analysis (MCCA), a popular technique used in data fusion, to select the optimal numbers of training days, similar days, and similar price days and by numerical experiments to determine the optimal number of neurons in the hidden layer. The spike forecaster is enhanced by having additional inputs including the predicted supply cushion, mined from information publicly available from the Ontario electricity market’s day-ahead System Status Report. The enhanced models are employed to conduct numerical experiments using data from the Ontario electricity market, which demonstrate significant improvements for forecasting accuracy.

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Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques

10.32920/ryerson.14649129 ◽

2021 ◽

Author(s):

Harmanjot Singh Sandhu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Electricity Markets ◽

Electricity Market ◽

Numerical Experiments ◽

Status Report ◽

Electricity Prices ◽

Forecasting Accuracy ◽

Using Data ◽

The Impact

Various machine learning-based methods and techniques are developed for forecasting day-ahead electricity prices and spikes in deregulated electricity markets. The wholesale electricity market in the Province of Ontario, Canada, which is one of the most volatile electricity markets in the world, is utilized as the case market to test and apply the methods developed. Factors affecting electricity prices and spikes are identified by using literature review, correlation tests, and data mining techniques. Forecasted prices can be utilized by market participants in deregulated electricity markets, including generators, consumers, and market operators. A novel methodology is developed to forecast day-ahead electricity prices and spikes. Prices are predicted by a neural network called the base model first and the forecasted prices are classified into the normal and spike prices using a threshold calculated from the previous year’s prices. The base model is trained using information from similar days and similar price days for a selected number of training days. The spike prices are re-forecasted by another neural network. Three spike forecasting neural networks are created to test the impact of input features. The overall forecasting is obtained by combining the results from the base model and a spike forecaster. Extensive numerical experiments are carried out using data from the Ontario electricity market, showing significant improvements in the forecasting accuracy in terms of various error measures. The performance of the methodology developed is further enhanced by improving the base model and one of the spike forecasters. The base model is improved by using multi-set canonical correlation analysis (MCCA), a popular technique used in data fusion, to select the optimal numbers of training days, similar days, and similar price days and by numerical experiments to determine the optimal number of neurons in the hidden layer. The spike forecaster is enhanced by having additional inputs including the predicted supply cushion, mined from information publicly available from the Ontario electricity market’s day-ahead System Status Report. The enhanced models are employed to conduct numerical experiments using data from the Ontario electricity market, which demonstrate significant improvements for forecasting accuracy.

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Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i6.pp4060-4078 ◽

2018 ◽

Vol 8 (6) ◽

pp. 4060

Author(s):

Francesco Arci ◽

Jane Reilly ◽

Pengfei Li ◽

Kevin Curran ◽

Ammar Belatreche

Keyword(s):

Electricity Markets ◽

Electricity Market ◽

Electricity Generation ◽

Informed Trading ◽

Electricity Prices ◽

Customer Demand ◽

Short Term ◽

Market Participants ◽

Energy Demands ◽

Trading Decisions

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.

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Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case

Energies ◽

10.3390/en14020364 ◽

2021 ◽

Vol 14 (2) ◽

pp. 364

Author(s):

Emma Viviani ◽

Luca Di Persio ◽

Matthias Ehrhardt

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Statistical Methods ◽

Probabilistic Method ◽

Energy Markets ◽

Price Forecasting ◽

Electricity Prices ◽

Electricity Price Forecasting ◽

Point Forecast ◽

Probabilistic Type

In this work, we investigate a probabilistic method for electricity price forecasting, which overcomes traditional ones. We start considering statistical methods for point forecast, comparing their performance in terms of efficiency, accuracy, and reliability, and we then exploit Neural Networks approaches to derive a hybrid model for probabilistic type forecasting. We show that our solution reaches the highest standard both in terms of efficiency and precision by testing its output on German electricity prices data.

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Particle Swarm Optimization Based GM(1,1) Method on Short-Term Electricity Price Forecasting with Predicted Error Improvement

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.40-41.183 ◽

2010 ◽

Vol 40-41 ◽

pp. 183-188

Author(s):

Rui Qing Wang ◽

Fu Xiong Wang ◽

Wen Tian Ji

Keyword(s):

Particle Swarm Optimization ◽

Electricity Market ◽

Particle Swarm ◽

Electricity Price ◽

Price Forecasting ◽

Short Term ◽

Swarm Optimization ◽

Forecasting Accuracy ◽

Market Participants ◽

Electricity Price Forecasting

Under deregulated environment, accurate electricity price forecasting is a crucial issue concerned by all market participants. Experience shows that single forecasting model is very difficult to improve the forecasting accuracy due to the complicated factors affecting electricity prices. In this paper, a particle swarm optimization based GM(1,1) method on short-term electricity price forecasting with predicted error improvement is proposed, in which the moving average method is used to process the raw data, the particle swarm optimization based GM(1,1) model is used to the processed series, and the time series analysis is used to further improve the predicted errors. The numerical example based on the historical data of the PJM market shows that the method can reflect the characteristics of electricity price better and the forecasting accuracy can be improved virtually compared with the conventional GM(1,1) model. The forecasted prices accurate enough to be used by electricity market participants to prepare their bidding strategies.

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Development of Neurofuzzy Architectures for Electricity Price Forecasting

Energies ◽

10.3390/en13051209 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1209 ◽

Cited By ~ 1

Author(s):

Abeer Alshejari ◽

Vassilis S. Kodogiannis ◽

Stavros Leonidis

Keyword(s):

New England ◽

Electricity Market ◽

Initial Step ◽

Electricity Price ◽

Power Markets ◽

Electricity Prices ◽

Electricity Price Forecasting ◽

Neuro Fuzzy ◽

Proposed Model ◽

Fuzzy Network

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision-making process as well as strategic planning. In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.

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A New Method for Next-Day Price Forecasting for PJM Electricity Market

International Journal of Emerging Electric Power Systems ◽

10.2202/1553-779x.2266 ◽

2010 ◽

Vol 11 (2) ◽

Cited By ~ 3

Author(s):

Phatchakorn Areekul ◽

Tomonobu Senju ◽

Hirofumi Toyama ◽

Shantanu Chakraborty ◽

Atsushi Yona ◽

...

Keyword(s):

Electricity Market ◽

Electricity Price ◽

Management Tool ◽

Price Forecasting ◽

Occurrence Time ◽

Price Variation ◽

Rough Sets Theory ◽

Market Participants ◽

Electricity Price Forecasting ◽

Deregulated Electricity Market

In the framework of the competitive electricity markets, electricity price forecasting is important for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested interval of the peak electricity price forecasting. Forecasting the peak price is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. This paper proposes new approach to reduce the prediction error at occurrence time of the peak electricity price, and aims to enhance the accuracy of the next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak electricity price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak electricity price. This method is examined by using the data of the PJM electricity market. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.

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