TY - GEN
T1 - A short-term load forecasting scheme based on auto-encoder and random forest
AU - Son, Minjae
AU - Moon, Jihoon
AU - Jung, Seungwon
AU - Hwang, Eenjun
N1 - Funding Information:
This research was supported by Korea Electric Power Corporation (Grant number: R18XA05).
Funding Information:
This research was supported by Korea Electric Power Corporation (Grant
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Recently, the smart grid has been attracting much attention as a viable solution to the power shortage problem. One of critical issues for improving its operational efficiency is to predict the short-term electric load accurately. So far, many works have been done to construct STLF (Short-Term Load Forecasting) models using a variety of machine learning algorithms. By taking many influential variables into account, they gave satisfactory results in predicting overall electric load pattern. But, they are still lacking in predicting minute electric load patterns. To overcome this problem, in this paper, we propose a new STLF model that combines Auto-Encoder (AE) based feature extraction and Random Forest (RF) and show its performance by carrying out several experiments for the actual power consumption data collected from diverse types of building clusters.
AB - Recently, the smart grid has been attracting much attention as a viable solution to the power shortage problem. One of critical issues for improving its operational efficiency is to predict the short-term electric load accurately. So far, many works have been done to construct STLF (Short-Term Load Forecasting) models using a variety of machine learning algorithms. By taking many influential variables into account, they gave satisfactory results in predicting overall electric load pattern. But, they are still lacking in predicting minute electric load patterns. To overcome this problem, in this paper, we propose a new STLF model that combines Auto-Encoder (AE) based feature extraction and Random Forest (RF) and show its performance by carrying out several experiments for the actual power consumption data collected from diverse types of building clusters.
KW - Auto-encoder
KW - Feature extraction
KW - Random forest
KW - Short-term load forecasting
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=85068593594&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21507-1_21
DO - 10.1007/978-3-030-21507-1_21
M3 - Conference contribution
AN - SCOPUS:85068593594
SN - 9783030215064
T3 - Lecture Notes in Electrical Engineering
SP - 138
EP - 144
BT - Applied Physics, System Science and Computers III - Proceedings of the 3rd International Conference on Applied Physics, System Science and Computers APSAC 2018
A2 - Ntalianis, Klimis
A2 - Vachtsevanos, George
A2 - Borne, Pierre
A2 - Croitoru, Anca
PB - Springer Verlag
T2 - 3rd International Conference on Applied Physics, System Science and Computers, APSAC 2018
Y2 - 26 September 2018 through 28 September 2018
ER -
