Abstract
This article presents a comparison of wind speed forecasting techniques, starting with the Auto-regressive Integrated Moving Average, followed by Artificial Intelligence-based techniques. The objective of this article is to compare these methods and provide readers with an idea of what method(s) to apply to solve their forecasting needs. The Artificial Intelligence-based techniques included in the comparison are Nearest Neighbors (the original method, and a version tuned by Differential Evolution), Fuzzy Forecasting, Artificial Neural Networks (designed and tuned by Genetic Algorithms), and Genetic Programming. These techniques were tested against twenty wind speed time series, obtained from Russian and Mexican weather stations, predicting the wind speed for 10 days, one day at a time. The results show that Nearest Neighbors using Differential Evolution outperforms the other methods. An idea this article delivers to the reader is: what part of the history of the time series to use as input to a forecaster? This question is answered by the reconstruction of phase space. Reconstruction methods approximate the phase space from the available data, yielding m (the system’s dimension) and τ (the sub-sampling constant), which can be used to determine the input for the different forecasting methods.
Highlights
The world’s population growth has taken the planet to unsustainable levels of pollution, mainly caused by the industrialization of the developing world
Those errors are reported for different forecasting tasks, using different error metrics, so we cannot objectively compare the performance of the methods we present in this article with those presented in the articles included in the survey
A set of forecasting methods based on Soft Computing and the comparison of their performances, using a set of wind speed time series available to the public, has been presented
Summary
The world’s population growth has taken the planet to unsustainable levels of pollution, mainly caused by the industrialization of the developing world. To solve this problem we need technological changes; aiming to solve this problem, humans have developed alternative ways of producing electrical and mechanical power, to be used in the industry. One direction where these sort of policies can be applied is to alternative sources of electrical energy, ones that limit carbon emissions [1]. The challenge is to integrate this intermittent power source into the electricity grid
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