Renewable Energy from Wind Farm Power Plants in Peru: Recent Advances, Challenges, and Future Perspectives

Cacciuttolo, Carlos; Cano, Deyvis; Guardia, Ximena; Villicaña, Eunice

doi:10.3390/su16041589

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…The KS test is used to ascertain the correlation of each modal component with Gaussian noise, thereby ignoring components close to noise. After filtering with (6) proposed by IEMD, the number of IMFs is reduced from M ′ to M.…”

Section: Improved Empirical Mode Decompositionmentioning

confidence: 99%

“…Similarly, China is on course to exceed its renewable energy target, with a record 290 GW installed in 2023 alone, aiming for renewables to make up over 50% of the new electricity consumption by 2025 [4]. Furthermore, Peru's varied geography and extensive coastline make it an ideal location for wind power, offering potential capacities of 20.5 GW onshore and 347 GW offshore [6].…”

Section: Introductionmentioning

confidence: 99%

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Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

Huan,

Deng,

Zhu

et al. 2024

Energies

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Accurate wind power forecasting (WPF) is critical in optimizing grid operations and efficiently managing wind energy resources. Challenges arise from the inherent volatility and non-stationarity of wind data, particularly in short-to-medium-term WPF, which extends to longer forecast horizons. To address these challenges, this study introduces a novel model that integrates Improved Empirical Mode Decomposition (IEMD) with an enhanced Transformer called TransIEMD. TransIEMD begins by decomposing the wind speed into Intrinsic Mode Functions (IMFs) using IEMD, transforming the scalar wind speed into a vector form that enriches the input data to reveal hidden temporal dynamics. Each IMF is then processed with channel attention, embedding, and positional encoding to prepare inputs for an enhanced Transformer. The Direct Embedding Module (DEM) provides an alternative viewpoint on the input data. The distinctive perspectives of IEMD and DEM offer interaction through cross-attention within the encoder, significantly enhancing the ability to capture dynamic wind patterns. By combining cross-attention and self-attention within the encoder–decoder structure, TransIEMD demonstrates enhanced proficiency in detecting and leveraging long-range dependencies and dynamic wind patterns, improving the forecasting precision. Extensive evaluations on a publicly available dataset from the National Renewable Energy Laboratory (NREL) demonstrate that TransIEMD significantly improves the forecasting accuracy across multiple horizons of 4, 8, 16, and 24 h. Specifically, at the 24 h forecast horizon, TransIEMD achieves reductions in the normalized mean absolute error and root mean square error of 4.24% and 4.37%, respectively, compared to the traditional Transformer. These results confirm the efficacy of integrating IEMD with attention mechanisms to enhance the accuracy of WPF.

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Section: Improved Empirical Mode Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

Huan,

Deng,

Zhu

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Maintaining this growth trajectory could result in wind energy meeting nearly one-third of the world's electricity needs by 2050. As discussed in [6], the wind energy industry stands out as the fastest-growing infrastructure worldwide, with promising prospects also because its generation costs have significantly declined over the past 15 years. This trend applies both to offshore and onshore [7] installations, nearing parity with traditional energy sources.…”

Section: Introductionmentioning

confidence: 99%

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Famoso,

Oliveri,

Brusca

et al. 2024

Energies

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This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

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Implementation of Renewable Energy from Solar Photovoltaic (PV) Facilities in Peru: A Promising Sustainable Future

Cacciuttolo,

Guardia,

Villicaña

2024

Sustainability

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In the last two decades, Peru has experienced a process of transformation in the sources of its energy matrix, increasing the participation of clean energy such as solar photovoltaic (PV), on-shore wind, biomass, and small hydro. However, hydropower and natural gas remain the main sources of electricity, whereas off-shore wind, biogas, waves, tidal, and geothermal sources are currently underdeveloped. This article presents the enormous potential of Peru for the generation of electrical energy from a solar source equivalent to 25 GW, as it has in one of the areas of the world with the highest solar radiation throughout the year. In addition, this article presents the main advantages, benefits, and considerations of the implementation of solar photovoltaic technology, with emphasis on (i) the potential of solar energy, showing the available potential and an installed capacity by the year 2024 equivalent to 398 MW, (ii) current solar energy sources, characterizing existing industrial solar photovoltaic (PV) energy plants, and (iii) future solar energy facilities projections, stating the portfolio of solar renewable energy plant projects to be implemented in the future considering an installed capacity of 7.2 GW by 2028. Additionally, lessons learned, challenges, and directions for the future development of solar energy in the country are presented. Finally, the article concludes that if Peru takes advantage of solar potential by considering a sustainable future perspective and implementing strategic land-use planning, the southern region will be transformed into a world-class territory for renewable energy development considering the hybridization of concentrated solar power (CSP) systems with solar photovoltaic (PV) systems and solar energy storage systems.

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Renewable Energy from Wind Farm Power Plants in Peru: Recent Advances, Challenges, and Future Perspectives

Cited by 4 publications

References 42 publications

Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Implementation of Renewable Energy from Solar Photovoltaic (PV) Facilities in Peru: A Promising Sustainable Future

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