Proceedings ArticleDOI
Comparison of different models for wind speed prediction
Elizabeta Lazarevska
- pp 5544-5549
Reads0
Chats0
TLDR
The paper presents several unconventional models for wind speed prediction based on fuzzy logic and neural network techniques, and although they all possess excellent approximation capabilities, the neural model based on incremental extreme learning machine has shown the best simulation results.Abstract:
The paper presents several unconventional models for wind speed prediction based on fuzzy logic and neural network techniques. First, two fuzzy models of a position and position-gradient type are built on the basis of different meteorological data such as solar radiation, relative humidity, ambient temperature, atmospheric pressure etc. In order to obtain the fuzzy models for wind speed prediction, Sugeno-Yasukawa identification algorithm was employed. Next, a neuro-fuzzy model for wind speed prediction was build, based on statistical learning theory. The model presents a fuzzy inference system of Takagi-Sugeno type that uses an extended relevance vector machine for learning its parameters and number of fuzzy rules. Finally, a neural network approach was applied to build two different models for wind speed prediction based on extreme learning machine techniques. Both neural models represent single layer feedforward neural networks, with different learning algorithms. The first one applies classic extreme learning machine and the second one uses incremental extreme learning machine philosophy. The obtained models are compared for their generalization performance and approximation capability, and although they all possess excellent approximation capabilities, the neural model based on incremental extreme learning machine has shown the best simulation results.read more
Citations
More filters
Journal ArticleDOI
Weather Forecasting for Renewable Energy System: A Review
R. Meenal,D. Binu,K.C. Ramya,Prawin Angel Michael,K. Vinoth Kumar,Eswar Rajasekaran,B. Sangeetha +6 more
Proceedings ArticleDOI
Comparison of Three Methods for Short-Term Wind Power Forecasting
Qin Chen,Komla A. Folly +1 more
TL;DR: It is shown that for the short-term wind power forecasting, the ARIMA method performs better than both the ANNs and ANFIS, and for longer time horizon, the performance of ARMA deteriorated as compared to the other two methods.
Journal ArticleDOI
Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data
TL;DR: A multi-step-ahead wind speed prediction correction method is proposed with consideration of the passing effects from wind speed at the previous time slot, which shows that the proposed model has the best performance under different time horizons.
References
More filters
Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Proceedings ArticleDOI
Extreme learning machine: a new learning scheme of feedforward neural networks
TL;DR: A new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses the input weights and analytically determines the output weights of SLFNs is proposed.
Journal ArticleDOI
A fuzzy-logic-based approach to qualitative modeling
Michio Sugeno,T. Yasukawa +1 more
TL;DR: A general approach to quali- tative modeling based on fuzzy logic is discussed, which proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model.
Journal ArticleDOI
Universal approximation using incremental constructive feedforward networks with random hidden nodes
TL;DR: This paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer.