An improved neural network-based approach for short-term wind speed and power forecast
TL;DR: An improved radial basis function neural network-based model with an error feedback scheme (IRBFNN-EF) for forecasting short-term wind speed and power of a wind farm, where an additional shape factor is included in the classic Gaussian basis function associated with each neuron in the hidden layer.
About: This article is published in Renewable Energy.The article was published on 2017-05-01. It has received 204 citations till now. The article focuses on the topics: Wind power & Wind speed.
Citations
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TL;DR: The proposed model has the best multistep prediction performance; compared to the other involved models, the proposed model is more effective and robust in extracting the trend information.
324 citations
TL;DR: An exhaustive review of artificial neural networks used in wind energy systems is presented, identifying the methods most employed for different applications and demonstrating that Artificial Neural Networks can be an alternative to conventional methods in many cases.
305 citations
TL;DR: The proposed EnsemLSTM is applied on two case studies data collected from a wind farm in Inner Mongolia, China, to perform ten-minute ahead utmost short term wind speed forecasting and one-hour ahead short term Wind speed forecasting, and Statistical tests of experimental results compared with other popular prediction models demonstrated the proposal can achieve a better forecasting performance.
272 citations
TL;DR: This study combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model and has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression.
231 citations
TL;DR: This work integrates the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and feeds the cap value into Fbprophet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic.
Abstract: COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it's not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively.
227 citations
Cites background from "An improved neural network-based ap..."
...Machine learning techniques for forecasting algorithms are a branch of computer science that is trained from historical data, such as artificial neural networks, deep learning, decision trees and Bayesian networks [19, 20, 21]....
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References
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Book•
16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
29,130 citations
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Abstract: In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's (1982) algorithm. We present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which shows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation.
5,288 citations
Book•
01 Jan 1996TL;DR: This text is the first to combine the study of neural networks and fuzzy systems, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.
Abstract: From the Publisher:
"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems."
-- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.
In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.
Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material.
Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.
977 citations
TL;DR: The proposed hybrid method based on improved empirical mode decomposition and GA-BP neural network can improve the forecasting accuracy and computational efficiency, which make it suitable for on-line ultra-short term (10 min) and short term (1 h) wind speed forecasting.
476 citations
TL;DR: In this paper, the Kalman filter is analyzed in order to find the best configuration for wind speed and wind power forecast, in a hindcast mode, with 2-year-long data sets of wind speed provided by a Numerical Weather Prediction (NWP) model and two anemometric stations located in the eastern Liguria (Italy).
415 citations