• Login
    View Item 
    •   DBS eSource Home
    • Masters Dissertations
    • Information & Communications Technology
    • View Item
    •   DBS eSource Home
    • Masters Dissertations
    • Information & Communications Technology
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Forecasting the electricity demand using machine learning algorithms

    View/Open
    msc_shaik_sa_2020.pdf (5.037Mb)
    Author
    Shaik, Safdhar Ali
    Date
    2020
    Degree
    MSc in Data Analytics
    URI
    https://esource.dbs.ie/handle/10788/4233
    Publisher
    Dublin Business School
    Rights holder
    http://esource.dbs.ie/copyright
    Rights
    Items in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
    Metadata
    Show full item record
    Abstract
    Forecasting the electricity demand has been the key task to reach the long-term goals of European Union. Inadequacy of accurate measurement of electricity demand which further causes the under generation or over generation of electricity and may also results in huge investments on energy resources. Predictive analysis and time series forecasting methods are used to overcome these difficulties. The aim of this paper is to short-term forecast the electricity demand using the seasonal auto-regressive Integrated moving average (SARIMA) and compare the results with the Multi-layer perceptron (MLP) model. The half-hourly dataset of London city is used for analyzing the electricity demand which is collected from UK power networks along with weather and holidays in the same city from November 2011 to February 2014. The forecasting plots has made based on the maximum, minimum and average consumption and then compare the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE) of SARIMA with the MLP model. During the evaluation MLP outperformed the SARIMA model and the prediction graphs are displayed using User Interface.
    Collections
    • Information & Communications Technology

    Browse

    All of DBS eSourceCommunities & CollectionsBy Issue DateAuthorsSupervisorTitlesSubjectsThis CollectionBy Issue DateAuthorsSupervisorTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2022  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV