no code implementations • 7 Jan 2024 • Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Jon A. R. Liisberg
Detecting behind-the-meter (BTM) equipment and major appliances at the residential level and tracking their changes in real time is important for aggregators and traditional electricity utilities.
no code implementations • 19 Oct 2023 • Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Andrew J. Black, Jon A. R. Liisberg, Julian Lemos-Vinasco
As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e. g., demand response and load profiling.
1 code implementation • 3 Oct 2023 • Nam Trong Dinh, Sahand Karimi-Arpanahi, S. Ali Pourmousavi, Mingyu Guo, Julian Lemos-Vinasco, Jon A. R. Liisberg
In this paper, we compare the most common existing sizing methods in the literature with a battery sizing model that incorporates the practical operation of a battery, that is, receding horizon operation.
no code implementations • 16 Sep 2023 • Nam Trong Dinh, Sahand Karimi-Arpanahi, Rui Yuan, S. Ali Pourmousavi, Mingyu Guo, Jon A. R. Liisberg, Julian Lemos-Vinasco
Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use of network assets.
no code implementations • 23 Sep 2021 • Rui Yuan, S. Ali Pourmousavi, Wen L. Soong, Giang Nguyen, Jon A. R. Liisberg
In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters.