Editorial: Sustainable development of energy, Water and Environment Systems

Piacentino, Antonio; Catrini, Pietro; Markovska, Nataša; Guzović, Zvonimir; Mathiesen, Brian Vad; Ferrari, Simone; Duić, Neven; Lund, Henrik

doi:10.1016/j.energy.2019.116432

Cited by 17 publications

(7 citation statements)

References 109 publications

(101 reference statements)

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“…Wind power technologies, as one of the popular renewable energies, have been advanced considerably and gained global attention [22]. With falling costs and large-scale production of generators, the deployment of wind energy is accelerating [70].…”

Section: Long Term Demand Planningmentioning

confidence: 99%

“…It included wording that encouraged more urgent greenhouse gas emissions cuts and promised more climate finance for developing countries to adapt to climate impacts [19]. research in these identified topics [20][21][22]. This identified topics are: It is necessary to emphasize that there is no clear boundary between certain topics, but there is mutual interaction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Paving the way for the Paris Agreement: Contributions of SDEWES science

Guzović

Duić

Piacentino

et al. 2023

Energy

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Section: Long Term Demand Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Paving the way for the Paris Agreement: Contributions of SDEWES science

Guzović

Duić

Piacentino

et al. 2023

Energy

Self Cite

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“…b) The search agent has to move around the finest neighborfor obtaining the finest fitness value based on equation (4).…”

Section: Mathematical Model Of Bsmso Algorithmmentioning

confidence: 99%

“…3 The statistical method for WPP imitates the relationship between the measurements of meteorologicaland historical data, which generates outputs via statistical methods whose parameters are determined using data without examining any physical phenomena. 4 The physical method of WPP focuses on describing the wind flow around and inside the wind farm, as well as utilizing the power curve of the manufacturer to propose a wind power output estimate. 5 WPP Systems' hybrid technique blends the two methods in order to maximize the benefits of both and improve predictions.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine power prediction via deep neural network using hybrid approach

Ahilan

Sujesh²,

Yarrapragada

2022

Proceedings of the Institution of Mechanical Engineers, Part A:

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Due to the chaotic nature of wind speed, short-term wind power prediction is a challengeable one. A reliable and accurate wind power prediction model is necessary for the wind turbine industry. Based on their improved ability to cope with complicated nonlinear issues, an increasing number of deep learning-based models are being explored for wind power prediction as artificial intelligence technologies, especially in deep neural networks. In this research, the wind power prediction model is divided into three stages. The first stage is to collect data from wind turbines. The second stage is to apply the optimal tuning of the Deep Neural Network (DNN) to predict the wind power. Here, the hybrid algorithm termedBird Swarm Merged Seagull Optimizer(BSMSO) stipulates DNN’s weight optimization points for wind power prediction; in addition, it reduces the time required for the same. Finally, the efficacy of the proposed BSMSO-DNN prediction model is proved by matching the statistical performance measures as regards toerror metrics with other existing techniques. The simulation results reveal thatthe proposed hybrid modelreduces the prediction errors significantly.

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“…Hence, an energy planning methodology needs to consider evaluating social, technical, economic, environmental, and political energy aspects across time, space, and scenarios, searching for a balance between stakeholders' priorities, nature preservation, and collective welfare 2 . An overview of papers related to sustainable development of energy, water, and environment systems is found in Reference 3.…”

Section: Introductionmentioning

confidence: 99%

A new methodology to estimate future water‐energy nexus based on artificial neural networks

Carvalho

Carneiro

2021

Int J Energy Res

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Summary We propose a new methodology to evaluate future electricity generation scenarios aiming to explore implications of different sets of power plants on the water‐energy nexus (W‐EN). We use two artificial neural networks: cascade forward back propagation and Kohonen neural networks (KNN). Initially, the future electricity production of the area under investigation is determined. Sequentially, water consumption is calculated for different combinations of plants. The scenarios with the lowest annual water consumption values are based only on wind and photovoltaic (PV) plants. Despite using only renewable energy, the inclusion of concentrating solar power in a future scenario brings a significant increase in water consumption, challenging the sustainability of this matrix. A conservative scenario (50% of electricity production by thermal power plants and 50% by renewable energy) shows the highest annual water consumption, also a barrier to sustainability.

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Editorial: Sustainable development of energy, Water and Environment Systems

Cited by 17 publications

References 109 publications

Paving the way for the Paris Agreement: Contributions of SDEWES science

Paving the way for the Paris Agreement: Contributions of SDEWES science

Wind turbine power prediction via deep neural network using hybrid approach

A new methodology to estimate future water‐energy nexus based on artificial neural networks

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