A new set of simple equations can fast-track the search for metal-organic frameworks (MOFs), a Nobel-Prize-winning class of ...

Buildings produce a large share of New York's greenhouse gas emissions, but predicting future energy demand—essential for reducing those emissions—has been hampered by missing data on how buildings ...

In the face of increasing global energy demands and the imperative for sustainable management, neural networks have emerged as a core tool for forecasting and optimising energy consumption in ...

Energy use in healthcare is a growing policy concern. Hospitals account for a significant share of public sector emissions, ...

Researchers have developed a new algorithmic model that can improve predictions of cooling demand for greener buildings. This ...

This article was originally published at The Empowerment Alliance and is re-published here with permission. For American ...

Abhay Gupta is co-founder and CEO of Bidgely, evolving energy analytics for utilities with the power of data and artificial intelligence. While these are certainly significant environmental wins, the ...

Net-demand energy forecasts are critical for competitive market participants, such as in the Electric Reliability Council of Texas (ERCOT) and similar markets, for several key reasons. For example, ...

The Aardvark Weather machine learning algorithm is much faster than traditional systems and can work on a desktop computer. When you purchase through links on our site, we may earn an affiliate ...