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Found 8 papers, 1 papers with code
Dynamic and Memory-efficient Shape Based Methodologies for User Type Identification in Smart Grid Applications
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.
A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market
The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets.
A New Time Series Similarity Measure and Its Smart Grid Applications
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.
On the Financial Consequences of Simplified Battery Sizing Models without Considering Operational Details
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.
Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes
Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use of network assets.
Optimal activity and battery scheduling algorithm using load and solar generation forecasts
Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed.
Three-Layer Joint Distributionally Robust Chance-Constrained Framework for Optimal Day-Ahead Scheduling of E-mobility Ecosystem
In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation in day-ahead for e-mobility ecosystems.
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications
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.
