Couverture de Forecasting of Electricity Consumption using Gaussian Processes
Titre du livre:

Forecasting of Electricity Consumption using Gaussian Processes

GlobeEdit (17-06-2014 )

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ISBN-13:

978-3-639-80666-3

ISBN-10:
3639806662
EAN:
9783639806663
Langue du livre:
Anglais
texte du rabat:
On a broad view, the problem of forecasting electricity consumption can be categorized under machine learning, which is the study of computer algorithms that improve automatically through experience. In order to predict how a trend will continue, the prediction model should be able to generalize the knowledge in historical data to unseen future. In this book, the following areas has been covered: ✪ The use of Gaussian Processes for electricity consumption forecasting ✪ Use of kNN similarity search with Gaussian Processes to reduce the size of the training data (reduce computational cost) ✪ Neural Networks for electricity consumption forecasting ✪ Exploratory Data Analysis for feature selection and visual analysis of data ✪ Combining kNN similarity search with the classical linear regression model to improve prediction accuracy. ✪ Comparison of different prediction models including Gaussian Processes, Neural Networks and Local Linear Regression.
Maison d'édition:
GlobeEdit
Site Web:
https://www.globeedit.com
de (auteur) :
Girma Kejela
Numéro de pages:
100
Publié le:
17-06-2014
Stock:
Disponible
Catégorie:
Informatique, IT
Prix:
39.90 €
Mots-clés:
machine learning, Neural Networks, Gaussian Processes, local linear regression, K-Nearest Neighbors, Electricity Consumption Forecasting, Feature selection, regression, prediction, Data analysis, linear regression, Data Structure, load forecasting, energy forecasting, Data Mininig, exploratory data analysis, KD tree, Gaussian

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