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Decisions and Market Analysis for Solar Energy
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Decisions and Market Analysis for Solar Energy

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 893

Special Issue Editors

Prof. Dr. Vittorio Ferraro

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Guest Editor
Department of Computer Engineering, Modelling, Electronics and Systems, University of Calabria, P. Bucci 42/C and 46/C, Arcavacata di Rende, 87036 Arcavacata, CS, Italy
Interests: solar energy; applied physics; renewable energy; daylighting; heat transfer in buildings; thermodynamic solar power plants, gas cycle; heat exchanger; photovoltaics; PV cooling systems; CFD analysis for thermal processes
Dr. Francesco Nicoletti

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, Via Pietro Bucci, 87036 Arcavacata, CS, Italy
Interests: solar plants; solar photovoltaics; solar thermal plants; concentrating solar plants; energy saving; thermal comfort of indoor spaces; thermal analysis of opaque walls; solar passive systems for buildings; solar shields; thermal modelling; application of artificial intelligence for energy purposes; optimization of heat pumps for residential buildings; daylight; heat exchangers
Prof. Dr. Dimitrios Kaliakatsos

E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, Via Pietro Bucci, 87036 Arcavacata, CS, Italy
Interests: energy building certification; passive cooling of buildings; daylight; passive acoustic requirements for buildings; solar radiation on the vertical walls of buildings; absorption heat pumps for air-conditioning; energy and economic assessments; solar thermal concentration systems; geothermal heat pump air-conditioning systems

Special Issue Information

Dear Colleagues,

Presently, the use of renewable sources for energy production is increasingly important in limiting climate change. Solar energy can make an important contribution in the decarbonisation of society. In the near future, a further rapid spread of energy production by different types of solar plants is expected (photovoltaics, solar thermal plants, thermodynamic solar plants). Their presence will become part of everyday processes in all sectors, including building, industry, transport, and power generation. They will therefore be integrated with other energy-production and -utilization systems so that their use can be planned and interconnected with other services. Moreover, their introduction will be more advantageous if they also lead to cost savings for users and for society.

The aim of this Special Issue is to strengthen the position of solar energy in the energy market, and to provide economical and technical solutions to make solar technologies profitable in the market.

Topics of interest for publication in this Special Issue include, but are not limited to, the following:

  • Solar energy market analysis.
  • Financing decisions for solar technologies.
  • Economic analysis of processes related to the production, transmission, and/or utilization of solar energy.
  • Technologies to increase production efficiency and make solar technology competitive in the market.
  • Urban-scale energy decisions to maximize self-consumption.
  • Solar energy utilization in renewable energy communities.
  • Solar energy utilization in smart homes.
  • Utilization of solar energy combined with other services (heat pumps, electric vehicles, household appliances, etc.).
  • Economic analysis for solar concentration plants.
  • Strategy for the use of solar energy in specific application fields.
  • Artificial neural networks for forecasting purposes and managing solar energy flows.

Prof. Dr. Vittorio Ferraro
Dr. Francesco Nicoletti
Prof. Dr. Dimitrios Kaliakatsos
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.

Keywords

  • solar energy
  • market analysis
  • strategic decision making
  • photovoltaic production
  • solar thermal energy
  • concentrating solar plants
  • predictive technologies

Published Papers (1 paper)

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Research

22 pages, 8401 KiB  
Article
Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support
by Francesco Nicoletti and Piero Bevilacqua
Energies 2024, 17(2), 466; https://doi.org/10.3390/en17020466 - 18 Jan 2024
Viewed by 619
Abstract
The day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. [...] Read more.
The day-ahead photovoltaic electricity forecast is increasingly necessary for grid operators and for energy communities. In the present work, the hourly PV production is estimated using two models based on feedforward neural networks (FFNNs). Most existing models use solar radiation as an input. Instead, the models proposed here use numerical weather prediction (NWP) data: ambient temperature, relative humidity, and wind speed, which are easily accessible to anyone. The first proposed model uses multiple inputs, while the second one uses only the necessary information. A sensitivity analysis allows for the identification of the variables that are most influential on the estimation accuracy. This study concludes that the hourly temperature trend is the most important variable for prediction. The models’ accuracy was tested using experimental and NWP data, with the second model having almost the same accuracy as the first despite using fewer input data. The results obtained using experimental data as inputs show a coefficient of determination (R2) of 0.95 for the hourly PV energy produced. The RMSE is about 6.4% of the panel peak power. When NWP data are used as inputs, R2 is 0.879 and the RMSE is 10.5%. These models can have a significant impact by enabling individual energy communities to make their forecasts, resulting in energy savings and increased self-consumed energy. Full article
(This article belongs to the Special Issue Decisions and Market Analysis for Solar Energy)
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