A Study on the Wind Energy Potential in the Romanian Coastal Environment

Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open‐source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short‐term memory (LSTM) for short‐term wind power forecasting based on the publicly accessible ERA5‐Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3‐hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.

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“…The Fântânele‐Cogealac WF is at a nameplate capacity of 600 MW Europe's most powerful onshore WF [39]. It is built up of 240 General Electric 2.5

\times

l WTs with a hub height of 100 m above ground level (AGL) that span a 3

\times

3 grid of ERA5‐Land points.…”

Section: Climate Datamentioning

confidence: 99%

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Marinšek

Bajt

2020

IET Renewable Power Generation

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“…Apart from the assessment of the wind energy potential, another objective of the present work is to estimate the performance of some offshore wind turbines, and therefore the U10 values need to be adjusted to the hub height of a particular system. This is indicated as [29,30]:…”

Section: Era-interim Reanalysis Datamentioning

confidence: 99%

An Assessment of Wind Energy Potential in the Caspian Sea

Onea

Rusu

2019

Energies

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At this time, there are plans to develop offshore wind projects in the Caspian Sea. The aim of the present work was to estimate the possible benefits coming from such a project. As a first step, the wind profile of this region was established by considering reanalysis data coming from the ERA-Interim project, the time interval covered being between January 1999 and December 2018. According to these results, significant resources have been noticed in the northern part where the wind speed frequently reached 8 m/s, being identified also as a hot-spot south of Olya site. In the second part, the performances of some offshore wind turbines were established. These were defined by rated capacities ranging from 3 MW to 8.8 MW. The downtime period of some generators can reach 90% in the central and southern sectors, while for the capacity factor, the authors expected a maximum of 33.07% for a turbine rated at 4.2 MW. From a financial point of view, the values of the levelized cost of energy (LCOE) indicated that the sites from the north and central parts of the Caspian Sea have been defined by an average LCOE of 0.25 USD/kWh. Thus, they can represent viable locations for wind farm projects.

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“…The energy strategy promoted by Romania has clear, well-defined objectives, through which Romania confirms its position as a producer of energy in the region and active factor in national energy management, which will produce substantial changes and will energize the entire energy sector [4,5].…”

Section: Introductionmentioning

confidence: 99%

Political strategies applied in Romania wind energy market in a medium and long run

Ţălu

Назаров

2021

E3S Web Conf.

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This paper analyses the main measures to be adopted in the field of wind energy in the medium and long term in Romania and assesses the progress made by Romania in comparison with the European Union and the objectives set by the Community institutions. The paper aims to identify opportunities for the development of the wind energy sector based on existing resources, which will be a positive factor in generating positive economic and social effects. The study indicates that Romania has a high potential for wind resources, which can be harnessed through appropriate policies to support projects related to this type of energy.

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A Study on the Wind Energy Potential in the Romanian Coastal Environment

Cited by 21 publications

References 34 publications

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Demystifying the use of ERA5‐land and machine learning for wind power forecasting

An Assessment of Wind Energy Potential in the Caspian Sea

Political strategies applied in Romania wind energy market in a medium and long run

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