TY - GEN
T1 - Machine Learning for Fast Short-Term Energy Load Forecasting
AU - Smith, Dominique
AU - Jaskie, Kristen
AU - Cadigan, John
AU - Marvin, Joseph
AU - Spanias, Andreas
N1 - Funding Information:
This work was supported in part by NSF Grant No. CNS 1659871 and NSF CPS REU supplement 1646542. The work was also supported in part by the ASU SenSIP Center and by the DOE Grant DE-SC0015094.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6/10
Y1 - 2020/6/10
N2 - Improved energy usage data from smart meters offers a unique opportunity to apply advanced analytics that can dramatically improve load forecasting. Utility companies, policy makers, and consumers benefit with better integration of renewables and overall energy management in the IoT digital age. Accurate short-term energy forecasting is essential to improving energy efficiency, reducing blackouts, and enabling smart grid control. In this work-in-progress (WIP) paper, we use individual residential load data to perform customer segmentation based on energy profiles, introduce a unique data segmentation and feature extraction technique based on inherent load signal periodicities, and use deep learning to perform fast and accurate short-term forecasting. Partnering with Prime Solutions Group, a veteran-owned company based in Arizona, we found that we could obtain up to a 12% improvement in hourly one-day forecasting using our custom data segmentation and feature extraction techniques with neural network methods.
AB - Improved energy usage data from smart meters offers a unique opportunity to apply advanced analytics that can dramatically improve load forecasting. Utility companies, policy makers, and consumers benefit with better integration of renewables and overall energy management in the IoT digital age. Accurate short-term energy forecasting is essential to improving energy efficiency, reducing blackouts, and enabling smart grid control. In this work-in-progress (WIP) paper, we use individual residential load data to perform customer segmentation based on energy profiles, introduce a unique data segmentation and feature extraction technique based on inherent load signal periodicities, and use deep learning to perform fast and accurate short-term forecasting. Partnering with Prime Solutions Group, a veteran-owned company based in Arizona, we found that we could obtain up to a 12% improvement in hourly one-day forecasting using our custom data segmentation and feature extraction techniques with neural network methods.
KW - LSTM
KW - Load Forecasting
KW - Machine Learning
KW - Neural Networks
KW - Smart Grid
UR - http://www.scopus.com/inward/record.url?scp=85098735358&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098735358&partnerID=8YFLogxK
U2 - 10.1109/ICPS48405.2020.9274781
DO - 10.1109/ICPS48405.2020.9274781
M3 - Conference contribution
AN - SCOPUS:85098735358
T3 - Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
SP - 433
EP - 436
BT - Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
Y2 - 10 June 2020 through 12 June 2020
ER -