Wind power field reconstruction from a reduced set of representative measuring points

Abstract

In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem's objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere midlatitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of

Monthly North Atlantic Sea Level Pressure Reconstruction Back to 1750 CE Using Artificial Intelligence Optimization

“…Joining statistics and computer science, artificial intelligence (AI) is a multidisciplinary field with different areas of expertise such as machine learning (LeCun et al 2015;Kadow et al 2020), and optimization (Swarnkar and Swarnkar 2019;Soto et al 2019). Regarding the latter, some optimization techniques (Vrugt and Robinson 2007;Eiben and Smith 2015) have been used in Earth and environmental sciences to improve solar and wind power forecasts at local scales (Salcedo-Sanz et al 2018), fill the gaps in observational datasets (Kadow et al 2020) or maximize the skill of climate field reconstructions (CFR) (Salcedo-Sanz et al 2019;Jaume-Santero et al 2020). In particular, evolutionary algorithms (inspired by natural selection processes) provide optimized solutions for high dimensional non-linear problems (Vrugt and Robinson 2007;Eiben and Smith 2015), including those in climate science (Salcedo-Sanz et al 2019).…”

“…In particular, evolutionary algorithms (inspired by natural selection processes) provide optimized solutions for high dimensional non-linear problems (Vrugt and Robinson 2007;Eiben and Smith 2015), including those in climate science (Salcedo-Sanz et al 2019). When combined with traditional methods, they are effective in the task of increasing the reconstruction skill of thermodynamic fields such as temperature (Salcedo-Sanz et al 2019;Jaume-Santero et al 2020), and dynamic fields such as wind speed (Salcedo-Sanz et al 2018), by performing an optimized selection of records from the available observing network.…”

Four heuristic optimization algorithms applied to wind energy: determination of Weibull curve parameters for three Brazilian sites

“…Hajibandeh et al [18] used the multicriteria multi-objective heuristic method to propose a new model for wind energy and DR integration, optimizing supply and demand side operations by the time to use (TOU) or incentive with the emergency DR program (EDRP), as well as combining TOU and EDRP together. Salcedo-Sanz et al [29] addressed a problem of representative selection of measurement points for long-term wind energy analysis, as the objective of selecting the best set of N measurement points, such that a measure of wind energy error reconstruction is minimized considering a monthly average wind energy field, for which the metaheuristic algorithm, Coral Reef Optimization with Substrate Layer, was used, which is an evolutionary type method capable of combining different search procedures within a single population. Faced with the inconsistent relationship between China's economy and the distribution of wind power potential that caused unavoidable difficulties in wind power transport and even network integration, Jiang et al [19] studied, by optimization methods, among them the Cuckoo Search and the Particle Swarm, the establishment of an integrated electric energy system with low-speed wind energy.…”