Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison
Open Access
- 16 September 2019
- Vol. 12 (18), 3545
- https://doi.org/10.3390/en12183545
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.This publication has 41 references indexed in Scilit:
- A Review of Wind Power Forecasting ModelsEnergy Procedia, 2011
- Mycielski approach for wind speed predictionEnergy Conversion and Management, 2009
- From probabilistic forecasts to statistical scenarios of short‐term wind power productionWind Energy, 2008
- On‐line assessment of prediction risk for wind power production forecastsWind Energy, 2004
- Support vector machines experts for time series forecastingNeurocomputing, 2003
- Fuzzy logic-based forecasting modelEngineering Applications of Artificial Intelligence, 2001
- A comparison of fuzzy forecasting and Markov modelingFuzzy Sets and Systems, 1994
- Fuzzy linear regression and its applications to forecasting in uncertain environmentFuzzy Sets and Systems, 1985
- Simple mathematical models with very complicated dynamicsNature, 1976
- Why do we Sometimes get Nonsense-Correlations between Time-Series?--A Study in Sampling and the Nature of Time-SeriesJournal of the Royal Statistical Society, 1926