Novel methods for wind speeds prediction across multiple locations

Abstract

This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

Improving extreme offshore wind speed prediction by using deconvolution

“…The countless strategies endeavoured included the method presented by Mohande [ 26 ], which predicts wind speeds using neural networks or the different approaches to analyse wind speed estimates by Yan et al, as in Refs. [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Similarly, the authors of this paper have also previously used different statistical approaches to estimate better extreme statistical values such as, i.e.…”

“…Similarly, the authors of this paper have also previously used different statistical approaches to estimate better extreme statistical values such as, i.e. wind speed, wave height, response and load [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ]. Furthermore, the authors developed a novel reliability approach to forecast COVID-19 epidemic levels, indicating the approach's versatility and reliability in various fields [ 14 , 16 , 26 ].…”

“…Similarly, other authors, as in Refs. [ [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] ], have attempted to use novel statistical approaches to estimate wind speed or pattern more accurately. Meanwhile, two recent and pertinent scientific articles [ 8 , 26 ] illustrated the importance of wind forecasting and strategies adopted to maximise efficiency.…”

“…This is in contrast to many traditional extrapolation methods widely used in engineering practice where an assumed probability function is used [ [1] , [2] , [3] , [4] , [5] , [6] , 30 ]. Some other examples also include the Pareto-based distribution peak over the threshold (POT) [ 1 ], The Naess-Gaidai (NG) method fitting procedure was used in averaged conditional exceedance rate (ACER) method [ [7] , [8] , [9] , [10] , [11] ], bivariate ACER fitting method [ [12] , [13] , [14] , [15] , [16] , 47 ], Weibull distribution-based fitting method [ 38 ] and Gumbel distribution based fitting method [ 17 ].…”

Novel methods for coupled prediction of extreme wind speeds and wave heights

“…Our work, however, deals with the general approach of extreme value theory in which no physical dynamics of the water waves are expected to play a significant role in driving the appearance of rare events, i.e. there is an expected universality of extreme events in a variety of different physical and natural systems [12][13][14][15][16][17] . Although beyond the scope of our work, there Some successful attempts have been to study extreme events in water waves (often called rogue or freak waves) with distributions uniquely determined by the dynamics of the physical system.…”