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Forecasting
Special Issues
Renewable Energy Forecasting: Innovations and Breakthroughs
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Renewable Energy Forecasting: Innovations and Breakthroughs

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

Deadline for manuscript submissions: 1 June 2026 | Viewed by 10635

Special Issue Editors

Prof. Dr. Grzegorz Mentel

E-Mail Website
Guest Editor
Department of Quantitative Methods, Rzeszow University of Technology, 35-959 Rzeszow, Poland
Interests: finance; modeling and simulation; risk analysis; econometric analysis; time series analysis; risk management and insurance; forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Zhao

E-Mail Website
Guest Editor
School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233041, China
Interests: data envelopment analysis; applied econometrics; econometric analysis; production economics; applied economics; efficiency analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to submit your paper for consideration in MDPI's Special Issue entitled “Renewable Energy Forecasting: Innovations and Breakthroughs”. This Special Issue will focus on new concepts of modeling and forecasting in the energy market in the context of renewable energy sources, identifying innovations in this area and innovative solutions. An additional topic of this Special Issue is the problem of econometrics modeling in the context of the energy market.

The main goal of this Special Issue is to develop ideas in the field of modeling and forecasting in the field of renewable energy sources. Therefore, its topic focuses on the use of econometric methods in forecasting the phenomena in question. The Special Issue mainly focuses on innovations and breakthroughs in the field of renewable energy and reducing greenhouse gas emissions.

Papers may cover global, regional, national, or even local topics that are of wider significance. Within this broad spectrum, topics of particular interest include the following:

  • Energy supply security and energy demand;
  • The quality and efficiency of energy services;
  • Energy technology innovation and diffusion;
  • Energy modeling and forecasting;
  • Energy analysis;
  • Energy planning and energy management;
  • Financial and behavioral aspects of the energy market;
  • Risks, returns, and investment across energy sectors;
  • Sustainable development.

Prof. Dr. Grzegorz Mentel
Dr. Xin Zhao
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 250 words) can be sent to the Editorial Office for assessment.

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 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 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.

Keywords

  • energy demand
  • energy optimization
  • energy modeling
  • energy efficiency
  • energy supply
  • energy market risk

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

30 pages, 4332 KB  
Article
Decentralized Physical Infrastructure Networks (DePINs) for Solar Energy: The Impact of Network Density on Forecasting Accuracy and Economic Viability
by Marko Corn, Anže Murko and Primož Podržaj
Forecasting 2025, 7(4), 77; https://doi.org/10.3390/forecast7040077 - 10 Dec 2025
Viewed by 1070
Abstract
This study explores the role of decentralized physical infrastructure networks (DePINs) in enhancing solar energy forecasting, focusing on how network density influences prediction accuracy and economic viability. Using machine learning models applied to production data from 47 residential PV systems in Utrecht, Netherlands, [...] Read more.
This study explores the role of decentralized physical infrastructure networks (DePINs) in enhancing solar energy forecasting, focusing on how network density influences prediction accuracy and economic viability. Using machine learning models applied to production data from 47 residential PV systems in Utrecht, Netherlands, we developed a hierarchical forecasting framework: Level 1 (clear-sky baseline without historical data), Level 2 (solo forecasting using only local historical data), and Level 3 (networked forecasting incorporating data from neighboring installations). The results show that networked forecasting substantially improves accuracy: under solo forecasting conditions (Level 2), the Random Forests model reduces Mean Absolute Error (MAE) by 17% relative to the Level 1 baseline, and incorporating all available neighbors (Level 3) further reduces the MAE by an additional 34% relative to Level 2, corresponding to a total improvement of 45% compared with Level 1. The largest accuracy gains arise from the first 10–15 neighbors, highlighting the dominant influence of local spatial correlations. These forecasting improvements translate into significant economic benefits. Imbalance costs decrease from EUR 1618 at Level 1 to EUR 1339 at Level 2 and further to EUR 884 at Level 3, illustrating the financial impact of both solo and networked data sharing. A marginal benefit analysis reveals diminishing returns beyond approximately 10–15 neighbors, consistent with spatial saturation effects within 5–10 km radii. These findings provide a quantitative foundation for incentive mechanisms in DePIN ecosystems and demonstrate that privacy-preserving data sharing mitigates data fragmentation, reduces imbalance costs for energy traders, and creates new revenue opportunities for participants, thereby supporting the development of decentralized energy markets. Full article
(This article belongs to the Special Issue Renewable Energy Forecasting: Innovations and Breakthroughs)
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17 pages, 1581 KB  
Article
Modeling CO2 Emission Forecasting in Energy Consumption of the Industrial Building Sector under Sustainability Policy in Thailand: Enhancing the LISREL-LGM Model
by Chaiyan Junsiri, Pruethsan Sutthichaimethee and Nathaporn Phong-a-ran
Forecasting 2024, 6(3), 485-501; https://doi.org/10.3390/forecast6030027 - 24 Jun 2024
Cited by 6 | Viewed by 3559
Abstract
This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO2 emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with [...] Read more.
This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO2 emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with Thailand’s goals for sustainability development, as outlined in the green economy objectives. The research employs a quantitative research approach, utilizing Linear Structural Relationships based on a Latent Growth Model (LISREL-LGM model) which is a valuable tool for efficient country management towards predefined green economy objectives by 2033. The research findings reveal continuous significant growth in the past economic sector (1990–2023), leading to subsequent growth in the social sector. Simultaneously, this growth has had a continuous detrimental impact on the environment, primarily attributed to the economic growth in the industrial building sector. Consequently, the research indicates that maintaining current policies would result in CO2 emissions from energy consumption in the industrial building sector exceeding the carrying capacity. Specifically, the growth rate (2033/2024) would increase by 28.59%, resulting in a surpassing emission of 70.73 Mt CO2 Eq. (2024–2033), exceeding the designated carrying capacity of 60.5 Mt CO2 Eq. (2024–2033). Therefore, the research proposes strategies for country management to achieve sustainability, suggesting the implementation of new scenario policies in the industrial building sector. This course of action would lead to a reduction in CO2 emissions (2024–2033) from energy consumption in the industrial building sector to 58.27 Mt CO2 Eq., demonstrating a decreasing growth rate below the carrying capacity. This underscores the efficacy and appropriateness of the LISREL-LGM model employed in this research for guiding decision making towards green economy objectives in the future. Full article
(This article belongs to the Special Issue Renewable Energy Forecasting: Innovations and Breakthroughs)
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23 pages, 3484 KB  
Article
Forecasting Thailand’s Transportation CO2 Emissions: A Comparison among Artificial Intelligent Models
by Thananya Janhuaton, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao
Forecasting 2024, 6(2), 462-484; https://doi.org/10.3390/forecast6020026 - 20 Jun 2024
Cited by 16 | Viewed by 4316
Abstract
Transportation significantly influences greenhouse gas emissions—particularly carbon dioxide (CO2)—thereby affecting climate, health, and various socioeconomic aspects. Therefore, in developing and implementing targeted and effective policies to mitigate the environmental impacts of transportation-related carbon dioxide emissions, governments and decision-makers have focused on [...] Read more.
Transportation significantly influences greenhouse gas emissions—particularly carbon dioxide (CO2)—thereby affecting climate, health, and various socioeconomic aspects. Therefore, in developing and implementing targeted and effective policies to mitigate the environmental impacts of transportation-related carbon dioxide emissions, governments and decision-makers have focused on identifying methods for the accurate and reliable forecasting of carbon emissions in the transportation sector. This study evaluates these policies’ impacts on CO2 emissions using three forecasting models: ANN, SVR, and ARIMAX. Data spanning the years 1993–2022, including those on population, GDP, and vehicle kilometers, were analyzed. The results indicate the superior performance of the ANN model, which yielded the lowest mean absolute percentage error (MAPE = 6.395). Moreover, the results highlight the limitations of the ARIMAX model; particularly its susceptibility to disruptions, such as the COVID-19 pandemic, due to its reliance on historical data. Leveraging the ANN model, a scenario analysis of trends under the “30@30” policy revealed a reduction in CO2 emissions from fuel combustion in the transportation sector to 14,996.888 kTons in 2030. These findings provide valuable insights for policymakers in the fields of strategic planning and sustainable transportation development. Full article
(This article belongs to the Special Issue Renewable Energy Forecasting: Innovations and Breakthroughs)
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