Forecasting Electricity Prices: A Machine Learning Approach

Castelli, Mauro; Groznik, Aleš; Popovič, Aleš

doi:10.3390/a13050119

Cited by 12 publications

(7 citation statements)

References 55 publications

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“…Hence, the empirical results obtained reveal that the hypotheses included in the study are validated. These results are consistent with the current literature, e.g., [5,15,16,21,32,[52][53][54].…”

Section: Discussion and Policy Implicationssupporting

confidence: 93%

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

et al. 2022

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The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine learning algorithms present higher prediction performance than time series models for both periods, (ii) renewable sources are the most influential factor for the electricity prices, and (iii) the COVID-19 pandemic caused a change in the importance order of influential factors on the electricity prices. Thus, the empirical results highlight the consideration of machine learning algorithms in electricity price prediction. Based on the empirical results obtained, potential policy implications are also discussed.

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Section: Discussion and Policy Implicationssupporting

confidence: 93%

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

et al. 2022

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“…Sheha et al presented various models for the proactive prediction of energy demand in entire cities [429]. Castelli et al focused on improving the accuracy of electricity price forecasting by employing machine learning techniques that combine standard regression techniques with genetic algorithms [430]. Additional studies on this topic can be found in [406,[431][432][433][434].…”

Section: Modeling the Energy Market: From Power Grid Data To Energy P...mentioning

confidence: 99%

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

et al. 2023

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To face the impact of climate change in all dimensions of our society in the near future, the European Union (EU) has established an ambitious target. Until 2050, the share of renewable power shall increase up to 75% of all power injected into nowadays’ power grids. While being clean and having become significantly cheaper, renewable energy sources (RES) still present an important disadvantage compared to conventional sources. They show strong fluctuations, which introduce significant uncertainties when predicting the global power outcome and confound the causes and mechanisms underlying the phenomena in the grid, such as blackouts, extreme events, and amplitude death. To properly understand the nature of these fluctuations and model them is one of the key challenges in future energy research worldwide. This review collects some of the most important and recent approaches to model and assess the behavior of power grids driven by renewable energy sources. The goal of this survey is to draw a map to facilitate the different stakeholders and power grid researchers to navigate through some of the most recent advances in this field. We present some of the main research questions underlying power grid functioning and monitoring, as well as the main modeling approaches. These models can be classified as AI- or mathematically inspired models and include dynamical systems, Bayesian inference, stochastic differential equations, machine learning methods, deep learning, reinforcement learning, and reservoir computing. The content is aimed at the broad audience potentially interested in this topic, including academic researchers, engineers, public policy, and decision-makers. Additionally, we also provide an overview of the main repositories and open sources of power grid data and related data sets, including wind speed measurements and other geophysical data.

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“…Energy price forecasting became a hot topic, especially in the past few years. The deregulation of the electricity market increased significantly the need for the accurate forecast of electricity prices, since the quality of the forecast has high importance and makes big impact on the risk management and the pricenegotiation processes of the market participants [1]. On the other hand, with the increasing percentage of renewable energy sources in the system, trading strategy based on energy price forecast may influence the storage and control strategies, as well [6].…”

Section: Introductionmentioning

confidence: 99%

Hourly electricity price forecast for short-and long-term, using deep neural networks

Dombi¹,

Dulai

2022

Acta Universitatis Sapientiae, Informatica

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Despite the practical importance of accurate long-term electricity price forecast with high resolution - and the significant need for that - only small percentage of the tremendous papers on energy price forecast attempted to target this topic. Its reason can be the high volatility of electricity prices and the hidden – and often unpredictable – relations with its influencing factors. In our research, we performed different experiments to predicate hourly Hungarian electricity prices using deep neural networks, for short-term and long-term, too. During this work, investigations were made to compare the results of different network structures and to determine the effect of some environmental factors (meteorologic data and date/time - beside the historical electricity prices). Our results were promising, mostly for short-term forecasts - especially by using a deep neural network with one ConvLSTM encoder.

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Forecasting Electricity Prices: A Machine Learning Approach

Cited by 12 publications

References 55 publications

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

Hourly electricity price forecast for short-and long-term, using deep neural networks

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