This work explores a Principal Component Analysis (PCA) in combination with two post-processing techniques for the prediction of wind power produced over Sicily, and of solar irradiance produced over the Oklahoma Mesonet. For wind power, the study is conducted over a 2-year long period, with hourly data of the aggregated wind power output of the island. The 0-72 hour wind predictions are generated with the limited-area Regional Atmospheric Model System (RAMS), with boundary conditions provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic forecast. For solar irradiance, we consider daily data of the aggregated solar radiation energy output (based on the Kaggle competition dataset) over an 8-year long period. Numerical Weather Prediction data for the contest come from the National Oceanic & Atmospheric Administration -Earth System Research Laboratory (NOAA/ESRL) Global Ensemble Forecast System (GEFS) Reforecast Version 2. The PCA is applied to reduce the datasets dimension. A Neural Network (NN) and an Analog Ensemble (AnEn) post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction.
<p>The decarbonization objectives set by the EU Green Deal to increase the renewable generation heavily rely on the contribution of wind energy, both onshore, through the installation of new plants and repowering of existing plants, and offshore. The issuance of the new "Fit for 55" package of measures will result in an increase in the objectives already identified for 2030 for Italy, which in all probability will be set at over 21 GW of installed capacity for onshore wind (i.e., doubling the currently operating power) and at least 3 GW for offshore wind.&#160;An informed energy planning of the territory is therefore paramount to efficiently maximize renewable penetration. In these regards, the development of informatic tools aimed at disentangling both resource availability and generation potential can effectively play a key role in supporting optimal technology displacement through space. RSE has worked on these themes since the end of the 1990s, when the first version of the Italian Wind Atlas (ATLAEOLICO) WebGIS was released, providing a support tool for adequate energy planning of the territory. Throughout the years, the Wind Atlas has represented a reference for various stakeholders (wind plants developers, authorities responsible for spatial planning and companies involved in the electricity grid development) who recognized its great utility in quickly identifying the most suitable Italian areas for wind energy exploitation in terms of long-term annual average wind speed and full load hours. &#160;With the purpose that this platform keeps providing tangible support for energy planning, we have worked on both renewing the anemological database and the WebGIS structure, which is the focus of this work. &#160;The new Italian Wind Atlas AEOLIAN provides for a new anemological database consisting in 30 years (1990-2019) of hourly wind data at 1.4 km horizontal resolution (WGS84 UTM32) covering the whole Italian territory and marine areas. Wind trajectories are estimated through the Weather Research and Forecasting (WRF) meteorological model combined with a statistical post-processing based on Analog Ensemble (AnEn).&#160;The renewed AEOLIAN WebGIS, developed through the open access framework TerriaJS, integrates standard functions for visualizing and querying data, data download functions and advanced tools to support local energy planning. It shows the spatial distribution of onshore and offshore wind speed [m/s] and full load hours [MWh/MW]. Each variable is computed as the 30 years annual average at the heights of 50, 75, 100, 125 e 150 m. a.s.l. Within AEOLIAN, users can download both variable maps and historical series of wind speed for more accurate evaluations.&#160;Besides maps, AEOLIAN also includes a tool for the technical and economical evaluation of a hypothetical wind farm at a local scale. This tool allows assessing the energy performances in terms of the net annual energy production and the average cost of the energy produced, considering local distribution of the wind resource, energy performances of the wind farm and investments and management costs. &#160;</p>
Recent developments in terms of meteorological models and wind generation technologies call for an improved representation of the spatiotemporal variability of the wind conditions, in order to efficiently support strategic planning at the national scale. To do so, we have developed the new Italian Wind Atlas – Atlante EOLico ItaliANo (AEOLIAN), building upon its previous release at the beginning of the century, with the main goal of supporting the operators in planning the future wind generation, in accordance with the ambitious targets set for 2030 by the EU Green Deal (over 20 GW of installed onshore wind power, plus about 3 GW offshore). The new database is the results of a collaboration effort between Ricerca sul Sistema Energetico (RSE) SpA and the National Center for Atmospheric Research (NCAR), which jointly developed a novel approach combining Weather Research and Forecasting (WRF) based numerical weather modeling with the Analog Ensemble (AnEn) statistical technique. To create the atlas, model simulations have been initially carried out at hourly time-step over the 1990-2019 period with a horizontal resolution of 4 km, nesting a 1.33 km inner grid only for the 2015-2019 period. AnEn is then employed to extend in the past the 5 high-resolution years, creating a 30-year dataset which embeds the whole country, including the marine areas. Compared to similar products currently available, the new Atlas provides higher horizontal resolution and enhanced accuracy, thanks to the assimilation of the observational wind data of the Italian regional weather network in the simulations. Preliminary results are shown here by comparing AEOLIAN with other atlases recently developed at international level such as the New European Wind Atlas (NEWA), and by highlighting the differences with respect to the original Italian Wind Atlas. Some derived parameters of particular interest for the operators are also featured and shown, such as wind power density.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.