Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

Vega-Márquez, Belén; Rubio-Escudero, Cristina; Nepomuceno-Chamorro, Isabel A.; Arcos-Vargas, Ángel

doi:10.3390/app11136097

Cited by 14 publications

(7 citation statements)

References 40 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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“…Also, three commonly used Figures of Merit [41] [42], also called FOMs, have been taken into account to evaluate the proposed models:…”

Section: Prediction Modelsmentioning

confidence: 99%

Noisy Neighbour Impact Assessment and Prevention in Virtualized Mobile Networks

Muro¹,

Baena²,

Fortes³

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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The generalization in the use of virtualization in the upcoming generation of cellular networks involves new paradigms and approaches for their management. The correct sharing of the underlying resources between multiple virtualized network functions as well as any other processes sharing the same computational platform implies a complex architecture that makes virtualization challenging. As a result, numerous variables (e.g., computational capacity) were mostly ignored by previous management systems, but that can now lead to relevant impacts on the service performance. In this virtualized scenario with multiple coexistent processes over the same hardware, a "Noisy Neighbour" (NN) is identified as an entity that uses most of the underlying resources while other virtual units suffer a lack of them. While difficult to identify, such situations can affect the network service. In this context, the present work analyzes and assesses the NN problem for 5G Core scenarios. A complete emulated 5G network and analysis framework is defined and developed to evaluate the impact of a noisy entity. In this way, the degradation that Key Performance Indicators suffer in the network and by the end-users when a NN appears is assessed. Thus, the present work proposes a baseline for handling NN through a novel lifecycle management flow. For this purpose, it is evaluated the effectiveness of multiple Machine Learning (ML) models for the identification of NN, based on the metrics gathered from the proposed framework, achieving 99% of accuracy. Moreover, ML is applied to develop a method for network performance inference, along with a prediction model to forecast the number of CPU resources the network may demand at any given time supporting the proposed management flow.

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“…According to the international electric power freedom ranking (ABRACEEL), more than fifty countries have already reorganized their electrical systems, shifting from a vertically integrated structure of state companies to a structure of separate businesses (unbundling) [5]. With this deregulation of the electricity markets, competition was introduced for the generation and retail businesses.…”

Section: Related Workmentioning

confidence: 99%

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market

Junior¹,

Figueiredo²,

Seruffo³

et al. 2023

Learn. Nonlin. Mod.

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The essential difference between the Free Contracting Environment (FCE) and the Regulated Contracting Environment (RCE) is the possibility of freely negotiating energy terms and prices with suppliers. Disconnected from the tariffs regulated by the government, in the FCE, consumers bear the costly difference between the contracted energy and that consumed. This cost can be reduced with accurate knowledge of the consumer profile, based on the analysis of historical data. In this article, a methodology is proposed to evaluate the migration of consumers to the FCE. In a case study, graphical statistical techniques help identify the profile of a consumer in the city of Rio de Janeiro, subgroup A4 and with green tariff modality, in the period from 2016 to 2019. Then, classical and artificial neural network-based methods are used for consumption forecasting twelve months ahead. In particular, Long and Short Term Memories (LSTM) networks performed better than Autoregressive Integrated Moving Average (ARIMA) models. At the end, it is demonstrated with economic and financial indicators, the right decision of this consumer to migrate to the FCE, prior to the analysis performed in this case study.

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Use of Deep Learning Architectures for Day-Ahead Electricity Price Forecasting over Different Time Periods in the Spanish Electricity Market

Cited by 14 publications

References 40 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

Noisy Neighbour Impact Assessment and Prevention in Virtualized Mobile Networks

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market

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