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Forecasting of Electricity Consumption Using Gaussian Processes by Kejela Girma - Paperback
246.75 AED

Forecasting of Electricity Consumption Using Gaussian Processes by Kejela Girma - Paperback

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246.75 AED 

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Category Type
Information Technology
ISBN
9783639806663
Author
Kejela Girma
Publisher
Globeedit
Description:

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 ...

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PRODUCT INFORMATION

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    Specifications

    Category Type
    Information Technology
    ISBN
    9783639806663
    Languages
    English
    Item EAN
    2724436919061
    People
    Author
    Kejela Girma
    Category Type
    Information Technology
    ISBN
    9783639806663
    Languages
    English
    Item EAN
    2724436919061
    People
    Author
    Kejela Girma
    People
    Publisher
    Globeedit
    Technical Information
    Binding
    Paperback
    Languages and countries
    Book Language
    English
    Read more
  •  

    Description:

    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

    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.

    Product Features:
    • Category: Information Technology
    • Binding: Paperback
    • Language of Text: English
    • Author(s): Kejela Girma
    • Publisher: Globeedit
    • ISBN: 9783639806663
    • Number of Pages: 100
    • Dimensions: 9 x 6 x 0.24 inches
 

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