Analyzing Land Use Change and Climate Data to Forecast Energy Demand for a Smart Environment

Archana Prasad, Aparna S. Varde, Raga Gottimukkala, Clement Alo, Pankaj Lal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Energy is essential for the sustainable development of nations. Increasing population growth, along with expected increases in duration and intensity of extreme weather, can increase energy demands. There is the potential for further interruption if companies do not appropriately account for an increase in demand, especially with the state and federal agencies implementing a transition to clean energy production by the end of the decade. In order to assess energy demand with changing variables, we conduct energy demand analysis in a moderate emissions scenario in the residential sector that consumes the most energy of all energy sectors. We assess changes in energy demand by comparing results from the data mining / machine learning techniques of Support Vector Machines (SVM) and Artificial Neural Network (ANN). Our results are helpful in contributing to sustainable energy goals in line with smart environment initiatives via greenness and energy efficiency.

Original languageEnglish
Title of host publicationProceedings of 2021 9th International Renewable and Sustainable Energy Conference, IRSEC 2021
EditorsTarik Chafiq, Abdelaaziz El Hibaoui, Mohamed Essaaidi, Youssef Zaz
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413190
DOIs
StatePublished - 2021
Event9th International Renewable and Sustainable Energy Conference, IRSEC 2021 - Virtual, Online, Morocco
Duration: 23 Nov 202127 Nov 2021

Publication series

NameProceedings of 2021 9th International Renewable and Sustainable Energy Conference, IRSEC 2021

Conference

Conference9th International Renewable and Sustainable Energy Conference, IRSEC 2021
Country/TerritoryMorocco
CityVirtual, Online
Period23/11/2127/11/21

Keywords

  • Climate Change
  • Demand Forecasting
  • Energy Efficiency
  • Machine Learning
  • Smart Environment
  • Sustainability

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