Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron

Authors
Moon, JihoonKim, YongsungSon, MinjaeHwang, Eenjun
Issue Date
12월-2018
Publisher
MDPI
Keywords
hybrid forecast model; electrical load forecasting; time series analysis; random forest; multilayer perceptron
Citation
ENERGIES, v.11, no.12
Indexed
SCIE
SCOPUS
Journal Title
ENERGIES
Volume
11
Number
12
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/71437
DOI
10.3390/en11123283
ISSN
1996-1073
Abstract
A stable power supply is very important in the management of power infrastructure. One of the critical tasks in accomplishing this is to predict power consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on the prediction scope, building type can also be an important factor since the same types of buildings show similar power consumption patterns. A university campus usually consists of several building types, including a laboratory, administrative office, lecture room, and dormitory. Depending on the temporal and external conditions, they tend to show a wide variation in the electrical load pattern. This paper proposes a hybrid short-term load forecast model for an educational building complex by using random forest and multilayer perceptron. To construct this model, we collect electrical load data of six years from a university campus and split them into training, validation, and test sets. For the training set, we classify the data using a decision tree with input parameters including date, day of the week, holiday, and academic year. In addition, we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration. Then, we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set. Through various experiments, we show that our hybrid forecast model performs better than other popular single forecast models.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Electrical Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Hwang, Een jun photo

Hwang, Een jun
공과대학 (전기전자공학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE