Page 87 - 2023-Vol19-Issue2

Received: 1 March 2023 | Revised: 8 April 2023 | Accepted: 8 April 2023

DOI: 10.37917/ijeee.19.2.10 Vol. 19 | Issue 2 | December 2023

Open Access

Iraqi Journal for Electrical and Electronic Engineering

Original Article

Energy Demand Prediction Based on Deep Learning

Techniques

Sarab Shanan Swide *, Ali F. Marhoon
Electrical Engineering Department, University of Basrah, Basrah, Iraq

Correspondance
*Sarab Shanan Swide
Electrical Engineering department
University of Basrah, Basrah, Iraq
Email: pgs.sarab.shanan@uobasrah.edu.iq

Abstract
The development of renewable resources and the deregulation of the market have made forecasting energy demand
more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several
time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart
grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about
generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since
many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in
forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can
precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number
of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an
ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their
ensemble, we showed how well the individual Random forest performed.

Keywords
Deep Learning, Energy Prediction, Random Forest.

I. INTRODUCTION about load imbalance. Thus, some improvement in STLF
accuracy can lead to lower power system costs and improved
As economic expansion picks up, so does the need for elec- power management effectiveness. Forecasting with a longer
trical energy [1], [2], [3]. Energy plants produce hazardous horizon is also beneficial for maintenance and power man-
exhausts and lose efficiency when there is a sudden need for agement methods. Operating costs are drastically reduced
more energy. Additionally, the estimation of electrical energy when accuracy is increased by 1% [4]. In order to change
consumption or load forecast is becoming more significant the prediction inaccuracy even slightly is interesting. An un-
with the development of renewable energy sources and smart derestimated or overestimated power might cause issues with
grids. Load forecasting, which aims to predict future load supply and demand equilibrium.
demand, comprises predicting the future behavior of the elec-
trical load. future will see the development of a single house, With the help of cutting-edge and clever computer tech-
a grid, an area, and maybe a full country. The prediction hori- nology, such as power prediction, the smart grid is designed
zon is the span of time across which this forecast is conducted to adjust supply in real-time to match demand. Strong ties
in one or more phases. exist between the spinning reserve and supply management.
After then, estimating is part of load prediction. the spinning
Short-term load forecasting (STLF), with lead durations reserve, which is important when demand increases unex-
of half an hour to a day, is typically required for programming pectedly or generators fail or break down. Whenever the
and energy transfer scheduling, unit allocation, and choices

This is an open-access article under the terms of the Creative Commons Attribution License,
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2023 The Authors.
Published by Iraqi Journal for Electrical and Electronic Engineering | College of Engineering, University of Basrah.

https://doi.org/10.37917/ijeee.19.2.10 |https://www.ijeee.edu.iq 83