Today I have publicly released the functions used to measure the accuracy performance in my journal paper Nonintrusive load monitoring (NILM) performance evaluation: A unified approach for accuracy reporting in Springer’s Energy Efficiency Journal (DOI 10.1007/s12053-014-9306-2). The Python 3 code is available in my GitHub repository.
NILM performance evaluation code released
Published by Stephen Makonin
Dr. Stephen Makonin is an Adjunct Professor in Engineering Science at Simon Fraser University (SFU) and philanthropist. He received his PhD in Computing Science at Simon Fraser University in 2014 in the area of computational sustainability and has been a software engineer for over 24 years working for various local/international industry clients. Stephen is a registered Professional Engineering (PEng) with Engineers and Geoscientists BC and a Senior Member of the IEEE. His research interests include computational sustainability and the understanding of socioeconomic issues that pertain to technological advancement. Stephen is an expert in machine learning, data engineering, data engineering, and a world-renowned researcher in non-intrusive load monitoring (NILM) and disaggregation. Currently, Stephen is a Vice-Chair of the IEEE Signal Processing Society Vancouver Chapter and serves as an Editorial Board Member of Nature's Scientific Data journal. View all posts by Stephen Makonin
