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Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS)

by Fouad Mountassir, Mohammed Bousmah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 22
Year of Publication: 2021
Authors: Fouad Mountassir, Mohammed Bousmah
10.5120/ijca2021921589

Fouad Mountassir, Mohammed Bousmah . Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS). International Journal of Computer Applications. 183, 22 ( Aug 2021), 27-34. DOI=10.5120/ijca2021921589

@article{ 10.5120/ijca2021921589,
author = { Fouad Mountassir, Mohammed Bousmah },
title = { Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS) },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2021 },
volume = { 183 },
number = { 22 },
month = { Aug },
year = { 2021 },
issn = { 0975-8887 },
pages = { 27-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number22/32058-2021921589/ },
doi = { 10.5120/ijca2021921589 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:33.257940+05:30
%A Fouad Mountassir
%A Mohammed Bousmah
%T Short Term Electricity Load Forecasting using Smart Grid AI-Prediction Simulator (SGAIPS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 22
%P 27-34
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electricity Demand Forecasting techniques with Artificial Intelligence based on Machine Learning and Deep Learning Models have been widely used in the past 20 years. These techniques play an important role in the construction of next generation of Smart Grid. However, applied machine learning in this real world presents challenges, it requires resources (Datasets from Smart Meters and other IoT-connected devices), skills (Machine Learning Algorithms, python or other programming languages) and knowledge (conventional load forecasting methods, intelligent load forecasting methods). For this purpose, we have developed, a new Smart Grid Artificial Intelligence Prediction Simulator called SGAIPS, that can provide a systematic short term electricity load forecasting based on Smart meters and Machine Learning algorithms. The starting point for using SGAIPS is the construction of dataset by loading or generating smart meters’ data. The next step is to create, train and test the machine learning models. And the final step is to predict the energy consumption. SGAIPS is a zero code Simulator for Smart Grid, that aims to simplify the creation and implementation of virtual smart meters, machine learning models and intelligent forecasting methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Short-Term Load Forecasting Artificial Intelligence Smart Meters Smart Grid Prediction Simulator.