Overview of policy and market dynamics for the deployment of renewable energy sources in Italy: Current status and future prospects

Esposito, Luca; Romagnoli, Giulia

doi:10.1016/j.heliyon.2023.e17406

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…In 2021, renewable energy was the primary source of energy production in the European Union, accounting for 41% of total production. Solid fuels (18%), natural gas (6%), and oil (3%) also contributed to energy production [2]. Energy production in EU countries is highly diverse.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

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In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

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“…The dominance of fossil fuels such as natural gas is gradually being questioned due to issues like pollution, dependence on producer states and cartels, rising prices, resource depletion, and reserve exhaustion (Capellán-Pérez et al, 2014; Day and Day, 2017; Höök and Tang, 2013). As a result, it has become essential to control consumption while promoting new alternatives like renewable energy sources (RESs), which are free from carbon emissions (Esposito and Romagnoli, 2023; Kung et al, 2019). This transition toward cleaner sources of energy is shaping a new energy landscape characterized by the relocation of energy systems and the emergence of new players and sources of energy.…”

Section: Introductionmentioning

confidence: 99%

A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

Rekik,

El Alimi

2023

Energy Exploration & Exploitation

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Renewable energy systems have emerged as a viable option to mitigate the environmental impacts of traditional fossil fuels. However, the intermittent nature of these renewables, such as solar and wind, makes it challenging to ensure a stable energy supply using only one type. Therefore, combining more than a single technology offers significant advantages in addressing the limitations associated with each individual system. Nevertheless, developing these systems requires substantial financial investments, making it crucial to identify the most suitable locations prior to installing them. In this article, the prime objective was to propose a preliminary evaluation of land suitability for constructing solar and wind hybrid facilities (PV–wind, PV–CSP, and CS–wind) in Tataouine, southern Tunisia. To this end, a GIS-based MCDA methodology was developed based on an extensive literature review and experts’ feedback while considering climate, topography, accessibility, and environmental factors. The results obtained revealed that the optimal area for a CSP–PV hybrid system is about 793 km2, indicating that this combination has the highest potential in terms of available resources and compatibility. On the other hand, well-suited locations for hosting CSP–wind and PV–wind systems covered areas of 412 and 333 km2, respectively. Such specific locations are capable of generating an annual technical potential of 316.169, 91.252, and 62.970 TWh for CSP–PV, CSP–wind, and PV–wind, respectively. Interestingly, comprising almost all of the most appropriate sites, Remada and Dhiba stand as the ideal locations for accommodating such hybrid systems. Considering this outcome, Tataouine can position itself as a model for renewable energy adoption in Tunisia. Therefore, it is imperative for policymakers, investors, and local communities to collaborate and embrace these hybrid systems to capitalize on this immense potential and pave the way for a greener and more prosperous future.

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“…Integrated systems are also critical to mitigating global warming and supporting environmental sustainability. Photovoltaic panels are suitable for use in sunny areas because they convert solar radiation into electricity [ 7 ]. Ideally suited for windy locations, wind farms convert the kinetic energy of the wind into energy.…”

Section: Introductionmentioning

confidence: 99%

“…The relationships among these parameters may be complex, making it challenging to improve the system's effectiveness. In addition, the volatility of sustainable energy sources, including wind and solar energy has an impact on the effectiveness of combined renewable energy systems [ 7 ]. To optimize the efficiency of combined sustainable power systems, improved algorithms that can control these complicated relationships and discover optimal solutions that balance the trade-offs between cost, accuracy, and environmental effect, must be developed [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Techno-economic analysis of an HRES with fuel cells, solar panels, and wind turbines using an improved Al-Biruni algorithm

He,

Ismail,

Leng

et al. 2023

Heliyon

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Overview of policy and market dynamics for the deployment of renewable energy sources in Italy: Current status and future prospects

Cited by 13 publications

References 24 publications

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

Techno-economic analysis of an HRES with fuel cells, solar panels, and wind turbines using an improved Al-Biruni algorithm

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