Smart Energy Using Machine Learning

smart-energy.md

Smart Energy Using Machine Learning

Applications

Solar (PV)

• Solar Forecast
• Prediction of customers who will buy solar (reduce sale cost)
• Insurance/Finance for buying solar

Non-intrusive Load Monitoring (NILM)

• Power Disaggregation Research, MIT LIDS Lab
• NILMTK: Non-Intrusive Load Monitoring Toolkit
• Jack Kelly and William Knottenbelt, Deep Neural Networks Applied to Energy Disaggregation, ACM BuildSys 2015, [Video], Code
• Michael Zeifman and Kurt Roth, Non-Intrusive Appliance Load Monitoring (NIALM): Review and Outlook, ICCE 2011.
• Convolutional Neural Network For Energy Disaggregation
• Hyungsul Kim, Manish Marwah, Martin Arlitt, Geoff Lyon and Jiawei Han, Unsupervised Disaggregation of Low Frequency Power Measurements, SDM 2011. (Using Factorial HMM to solve disaggregation problem)
• Non-intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey, Ahmed Zoha et al. 2012.

Energy Abnormaly Detection

• How do I use LSTM Networks for time-series anomaly detection?
• Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal and Gautam Shroff, LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

Data Sets

• The Almanac of Minutely Power Dataset, Version 1, Version 2
• UK Domestic Appliance-Level Electricity (UK-DALE) Dataset

Reference

• energy.gov
• Office Of Efficient Energy & Renewable Energy
• GreenTechMedia
• Electric Power Research Institute