[HTML][HTML] Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities
Despite widespread adoption and outstanding performance, machine learning models are
considered as “black boxes”, since it is very difficult to understand how such models operate …
considered as “black boxes”, since it is very difficult to understand how such models operate …
Survey on explainable AI: From approaches, limitations and applications aspects
In recent years, artificial intelligence (AI) technology has been used in most if not all domains
and has greatly benefited our lives. While AI can accurately extract critical features and …
and has greatly benefited our lives. While AI can accurately extract critical features and …
Conditional synthetic data generation for robust machine learning applications with limited pandemic data
Background: At the onset of a pandemic, such as COVID-19, data with proper
labeling/attributes corresponding to the new disease might be unavailable or sparse …
labeling/attributes corresponding to the new disease might be unavailable or sparse …
[HTML][HTML] Deep reinforcement learning with planning guardrails for building energy demand response
Building energy demand response is projected to be important in decarbonizing energy use.
A demand response program that communicates “artificial” hourly price signals to workers …
A demand response program that communicates “artificial” hourly price signals to workers …
Time series-based deep learning model for personal thermal comfort prediction
A Chennapragada, D Periyakoil, HP Das… - Proceedings of the …, 2022 - dl.acm.org
Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC
control in energy-efficient buildings. Individual comfort models, compared to average …
control in energy-efficient buildings. Individual comfort models, compared to average …
Offline-online reinforcement learning for energy pricing in office demand response: lowering energy and data costs
D Jang, L Spangher, T Srivistava, M Khattar… - Proceedings of the 8th …, 2021 - dl.acm.org
Our team is proposing to run a full-scale energy demand response experiment in an office
building. Although this is an exciting endeavor which will provide value to the community …
building. Although this is an exciting endeavor which will provide value to the community …
Machine learning for smart and energy-efficient buildings
Energy consumption in buildings, both residential and commercial, accounts for
approximately 40% of all energy usage in the United States, and similar numbers are being …
approximately 40% of all energy usage in the United States, and similar numbers are being …
Methodology for interpretable reinforcement learning model for HVAC energy control
Deep reinforcement learning (DRL) approaches have been used in various application
areas to improve efficiency, optimization, or automation. However, very little is known about …
areas to improve efficiency, optimization, or automation. However, very little is known about …
Improved dequantization and normalization methods for tabular data pre-processing in smart buildings
Ubiquitous deployment of IoT sensors marks a defining characteristic of smart buildings, for
they constitute the source of data on building operation, diagnosis, and maintenance. For …
they constitute the source of data on building operation, diagnosis, and maintenance. For …
Cdcgen: Cross-domain conditional generation via normalizing flows and adversarial training
How to generate conditional synthetic data for a domain without utilizing information about
its labels/attributes? Our work presents a solution to the above question. We propose a …
its labels/attributes? Our work presents a solution to the above question. We propose a …