Abstract
One key issue for stable power supply is to forecast electric load accurately. Since buildings of the same type show similar power consumption patterns, it should be considered for accurate electric load forecast. In particular, university buildings show various electric loads depending on time and other external factors. In this paper, we propose a short-term load forecast model for educational buildings using 2-stage predictive analytics for the effective operation of their power system. To do that, we collect the electric load data of five years from a university campus. Next, we consider the electric load pattern by using the moving average method according to the day of the week. Next, we predict the daily electric load using the random forest method and finally evaluate its performance using the time series cross-validation. The experimental results show that our forecasting model outperforms other competing methods in terms of prediction accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 219-226 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538636497 |
| DOIs | |
| Publication status | Published - 2018 May 25 |
| Event | 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 - Shanghai, China Duration: 2018 Jan 15 → 2018 Jan 18 |
Other
| Other | 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 18/1/15 → 18/1/18 |
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Keywords
- Electric Load Forecasting
- Forecasting Model
- Machine Learning
- Moving Average
- Random Forest
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Information Systems and Management
Cite this
A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. / Moon, Jihoon; Kim, Kyu Hyung; Kim, Yongsung; Hwang, Een Jun.
Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 219-226 8367120.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics
AU - Moon, Jihoon
AU - Kim, Kyu Hyung
AU - Kim, Yongsung
AU - Hwang, Een Jun
PY - 2018/5/25
Y1 - 2018/5/25
N2 - One key issue for stable power supply is to forecast electric load accurately. Since buildings of the same type show similar power consumption patterns, it should be considered for accurate electric load forecast. In particular, university buildings show various electric loads depending on time and other external factors. In this paper, we propose a short-term load forecast model for educational buildings using 2-stage predictive analytics for the effective operation of their power system. To do that, we collect the electric load data of five years from a university campus. Next, we consider the electric load pattern by using the moving average method according to the day of the week. Next, we predict the daily electric load using the random forest method and finally evaluate its performance using the time series cross-validation. The experimental results show that our forecasting model outperforms other competing methods in terms of prediction accuracy.
AB - One key issue for stable power supply is to forecast electric load accurately. Since buildings of the same type show similar power consumption patterns, it should be considered for accurate electric load forecast. In particular, university buildings show various electric loads depending on time and other external factors. In this paper, we propose a short-term load forecast model for educational buildings using 2-stage predictive analytics for the effective operation of their power system. To do that, we collect the electric load data of five years from a university campus. Next, we consider the electric load pattern by using the moving average method according to the day of the week. Next, we predict the daily electric load using the random forest method and finally evaluate its performance using the time series cross-validation. The experimental results show that our forecasting model outperforms other competing methods in terms of prediction accuracy.
KW - Electric Load Forecasting
KW - Forecasting Model
KW - Machine Learning
KW - Moving Average
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85048461574&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048461574&partnerID=8YFLogxK
U2 - 10.1109/BigComp.2018.00040
DO - 10.1109/BigComp.2018.00040
M3 - Conference contribution
AN - SCOPUS:85048461574
SP - 219
EP - 226
BT - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -