Peak-Load Forecasting for Small Industries: A Machine Learning Approach

• Published in: Sustainability Vol. 12; no. 16; p. 6539
• Main Authors: Kim, Dong-Hoon, Lee, Eun-Kyu, Qureshi, Naik Bakht Sania
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2020
• Subjects: Accuracy; Building automation; Feature selection; Forecasting techniques; Humidity; Machine learning; Mathematical models; Methods; Precipitation; Seasonal variations; Sustainability; Trends; Variables
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su12166539

Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.