Abstract: Accurate mid-term load forecasting at the building level is vital for the strategic planning, operation, and sustainability of modern power systems. Machine learning approaches often require ...

This is Part II of a 2-part workshop. To attend Part II, you must register for Part I: https://jhu.libcal.com/event/16197178 and Part II: https://jhu.libcal.com/event ...

One of the panels during Monday's data center workshop at DTECH. Photo by Jeremiah Karpowicz. Utilities are confronting a new wave of large-load growth tied to artificial intelligence, arriving at a ...

Join us to experiment, break things, and imagine new possibilities. Data Club meetings are meetings, not workshops. An introduction to a bit of software is followed by opportunities to try the ...

Given marching orders by the U.S. Energy Dept. to boost artificial intelligence sector growth, independent agency Federal Energy Regulatory Commission (FERC) now must absorb nearly 200 diverging power ...

80% of data analysis is cleaning and preparing data. A major part of that cleaning is data tidying—structuring datasets into a consistent, predictable format that simplifies analysis, modeling, ...

A data-driven framework was proposed in this paper to enhance the accuracy of load power forecasting and improve the economy and reliability of security-constrained unit commitment (SCUC) scheduling.

Raw needs to be preloaded in set_reference for it to work. Can be fixed by adding in raw.load_data() before first mne function.