A Comparison Between Reconstruction Methods for Generation of Synthetic Time Series Applied to Wind Speed Simulation

Neeraj Dhanraj Bokde,Nadhir Al-Ansari,Andres Feijoo,Zaher Mundher Yaseen

doi:10.1109/access.2019.2941826

Abstract

Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively.

Highlights

Human population increment, excessive pollution, depletion of fossil fuels and growing environmental concerns lead to a raise in the demand of renewable energies
The Step-wise reconstruction method is based on independent values generation from a known probability distribution, rearrangement of random values and segmentation processes, which return a synthetic time series with statistical characteristics very close to those of the original wind speed time series
The rearrangement in the Rank-wise method is of single step, reordering the random values based on the maximum positive rank correlation between the original and the generated variables

Summary

Introduction

Excessive pollution, depletion of fossil fuels and growing environmental concerns lead to a raise in the demand of renewable energies. Wind energy is of a great importance and potential owing to its availability and efficiency. Meteorological agencies collect wind speed data at many locations and make them available for wind energy research activities. The long period of historical data can be collected to perform the best research source. The major difficulty mainly associated with the unavailability of continuous time series data for a long time at specific locations. The simulation of numerical series under the constraint of keeping some of their statistical characteristics, such as

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Conclusion