Work

Short term power demand prediction using stochastic gradient boosting

Year: 2016

Type: conference-paper

Abstract: Power prediction demand is vital in power system and delivery engineering fields. By efficiently predicting the power demand, we can forecast the total energy to be consumed in a certain city or district. Thus, exact resources required to produce the demand power can be allocated. In this paper, a Stochastic Gradient Boosting (aka Treeboost) model is used to predict the short term power demand for the Emirate of Sharjah in the United Arab Emirates (UAE). Results show that the proposed model gives promising results in comparison to the model used by Sharjah Electricity and Water Authority (SEWA).

Source: International Conference on Electronic Devices, Systems and Applications

DOI: 10.1109/icedsa.2016.7818510

Author: Ali Bou Nassif

Institution: University of Sharjah

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2575574673

FWCI: 0.469

Cites: 18

Cited by: 17

Related to: 10