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Forecasting
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Advanced Methods for Renewable Energy Forecasting
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Advanced Methods for Renewable Energy Forecasting

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 1664

Special Issue Editor

Dr. Juan M. Lujano-Rojas

E-Mail Website
Guest Editor
Department of Electrical Engineering, Universidad de Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain
Interests: distributed power generation; battery storage plants; energy storage; optimisation; rural electrification

Special Issue Information

Dear Colleagues,

Prediction is a crucial procedure applied in many fields for the optimal management of assets and resources. Energy commodities are a vital element of human activities. They are optimally planned and managed by policymakers through a prediction process that includes the availability of natural resources and other relevant socio-economic parameters and indices.

The environmental problems related to coal, oil, and natural gas exploitation for power generation motivate policymakers to modify the energy mix to increase the portion of the energy produced from clean sources. The transition to an environmentally-friendly power system also relies on a forecasting process applied on different ranges from hours to years.

This special issue aims to collect state-of-the-art prediction techniques and studies mainly based on deep learning and artificial intelligence and assess their implementation for planning and managing renewable power systems, energy portfolios, energy stocks and markets, and natural resource assessment. In addition to deep learning algorithms, other methodologies based on statistical analysis and physical methods supported by weather information are also welcomed.

Dr. Juan M. Lujano-Rojas
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 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. Forecasting is an international peer-reviewed open access quarterly 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.

Published Papers (1 paper)

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Research

26 pages, 10217 KiB  
Article
Searching for Promisingly Trained Artificial Neural Networks
by Juan M. Lujano-Rojas, Rodolfo Dufo-López, Jesús Sergio Artal-Sevil and Eduardo García-Paricio
Forecasting 2023, 5(3), 550-575; https://doi.org/10.3390/forecast5030031 - 04 Sep 2023
Viewed by 1146
Abstract
Assessing the training process of artificial neural networks (ANNs) is vital for enhancing their performance and broadening their applicability. This paper employs the Monte Carlo simulation (MCS) technique, integrated with a stopping criterion, to construct the probability distribution of the learning error of [...] Read more.
Assessing the training process of artificial neural networks (ANNs) is vital for enhancing their performance and broadening their applicability. This paper employs the Monte Carlo simulation (MCS) technique, integrated with a stopping criterion, to construct the probability distribution of the learning error of an ANN designed for short-term forecasting. The training and validation processes were conducted multiple times, each time considering a unique random starting point, and the subsequent forecasting error was calculated one step ahead. From this, we ascertained the probability of having obtained all the local optima. Our extensive computational analysis involved training a shallow feedforward neural network (FFNN) using wind power and load demand data from the transmission systems of the Netherlands and Germany. Furthermore, the analysis was expanded to include wind speed prediction using a long short-term memory (LSTM) network at a site in Spain. The improvement gained from the FFNN, which has a high probability of being the global optimum, ranges from 0.7% to 8.6%, depending on the forecasting variable. This solution outperforms the persistent model by between 5.5% and 20.3%. For wind speed predictions using an LSTM, the improvement over an average-trained network stands at 9.5%, and is 6% superior to the persistent approach. These outcomes suggest that the advantages of exhaustive search vary based on the problem being analyzed and the type of network in use. The MCS method we implemented, which estimates the probability of identifying all local optima, can act as a foundational step for other techniques like Bayesian model selection, which assumes that the global optimum is encompassed within the available hypotheses. Full article
(This article belongs to the Special Issue Advanced Methods for Renewable Energy Forecasting)
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