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Energies
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Forecasting in Electricity Markets with Big Data and Artificial Intelligence
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Forecasting in Electricity Markets with Big Data and Artificial Intelligence

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (25 November 2021) | Viewed by 7712

Special Issue Editors

Dr. Andrés M. Alonso

E-Mail Website
Guest Editor
Institute Flores de Lemus and Department of Statistics, Universidad Carlos III de MadridCalle Madrid, 126, 28903 Getafe, Spain
Interests: time series analysis; resampling techniques; applied statistics and econometrics
Dr. Francisco Javier Nogales

E-Mail Website
Guest Editor
Institute UC3M-Santander of Financial Big Data and Department of Statistics, Universidad Carlos III de Madrid Avda. de la Universidad, 30, 28911 Leganés, Spain
Interests: big data optimization; quantitative portfolio management and analytics in energy markets
Dr. Carlos Ruiz

E-Mail Website
Guest Editor
Institute UC3M-Santander of Financial Big Data and Department of Statistics, Universidad Carlos III de Madrid Avda. de la Universidad, 30, 28911 Leganés, Spain
Interests: intersection of operations research, analytics, and energy systems

Special Issue Information

Dear Colleagues,

The latest analytical and computational tools for decision making under uncertainty have found an important field of application in power systems in the new Big Data era. In particular, these techniques can efficiently assist consumers and utilities to make informed decisions under new technological paradigms: the increased adoption of electric vehicles, the impact of weather on renewable energy sources, the integration of large-scale storage systems, the availability of consumption data from smart meters, and the adoption of demand response policies, among others.
This Special Issue aims to collect original research or review articles on:

  • Descriptive analytical tools and forecasting for smart meter data, consumption profiles, hourly day-ahead prices, weather patterns and their influence on consumption, etc.
  • Forecasting techniques for renewable energy, consumption, electricity prices, etc.
  • Machine learning tools (prediction, classification, clustering, etc.) to extract consumption profiles, cluster similar consumers, design of tariffs, demand response, etc.

Related topics may also be considered, and we recommend sending a tentative title and a short summary of the manuscript.

Assoc. Prof. Dr. Andrés M. Alonso
Assoc. Prof. Dr. Francisco Javier Nogales
Assoc. Prof. Dr. Carlos Ruiz
Guest Editors

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 submissions that pass pre-check are 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 2600 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.

Published Papers (3 papers)

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Research

17 pages, 1000 KiB  
Article
Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO
by Arkadiusz Jędrzejewski, Grzegorz Marcjasz and Rafał Weron
Energies 2021, 14(11), 3249; https://doi.org/10.3390/en14113249 - 02 Jun 2021
Cited by 7 | Viewed by 2596
Abstract
Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based [...] Read more.
Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component. Full article
(This article belongs to the Special Issue Forecasting in Electricity Markets with Big Data and Artificial Intelligence)
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19 pages, 1659 KiB  
Article
A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series
by Andrés M. Alonso, Francisco J. Nogales and Carlos Ruiz
Energies 2020, 13(20), 5328; https://doi.org/10.3390/en13205328 - 13 Oct 2020
Cited by 16 | Viewed by 2420
Abstract
Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models [...] Read more.
Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model. Full article
(This article belongs to the Special Issue Forecasting in Electricity Markets with Big Data and Artificial Intelligence)
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26 pages, 6315 KiB  
Article
Short-Term Load Forecasting for Spanish Insular Electric Systems
by Eduardo Caro and Jesús Juan
Energies 2020, 13(14), 3645; https://doi.org/10.3390/en13143645 - 15 Jul 2020
Cited by 4 | Viewed by 2024
Abstract
In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation agents. This study focuses on the development of [...] Read more.
In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation agents. This study focuses on the development of a prediction model to be applied to the ten main Spanish islands: seven insular systems in the Canary Islands, and three systems in the Balearic Islands. An exhaustive analysis is presented concerning both the estimation results and the forecasting accuracy, benchmarked against an alternative prediction software and a set of modified models. The developed models are currently being used by the Spanish TSO (Red Eléctrica de España, REE) to make hourly one-day-ahead forecasts of the electricity demand of insular systems. Full article
(This article belongs to the Special Issue Forecasting in Electricity Markets with Big Data and Artificial Intelligence)
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