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Found 9 papers, 1 papers with code
A Lightweight Energy Management Method for Hybrid PV/Battery/Load Systems
In this paper, a computationally lightweight algorithm is introduced for hybrid PV/Battery/Load systems that is price responsive, responds fast, does not require powerful hardware, and considers the operational limitations of the system.
Adaptive flexibility function in smart energy systems: A linearized price-demand mapping approach
This paper proposes an adaptive mechanism for price signal generation using a piecewise linear approximation of a flexibility function with unknown parameters.
Nash Equilibrium of Joint Day-ahead Electricity Markets and Forward Contracts in Congested Power Systems
In this paper, a Cournot Nash equilibrium model is proposed to study the behavior of market players in the forward contract market and the day-ahead electricity market in a congested power system with large-scale integration of WPPs.
Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities
This demonstrates the relevance and value of our approach in connecting occupants to cities for improved, and more resilient, urban energy planning strategies.
Structural hierarchical learning for energy networks
Overall, this work expands and improves hierarchical learning methods thanks to a structurally-scaled learning mechanism extension coupled with tailored network designs, producing a resourceful, data-efficient, and information-rich learning process.
Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads
With this work, we propose a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors and established hierarchical reconciliation taxonomy.
Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets
It is demonstrated using monitoring data from a refrigeration system in a supermarket consisting of data sampled at a one-minute sampling rate, however it is shown that a sampling time of around 10-20 minutes is adequate for the method.
onlineforecast: An R package for adaptive and recursive forecasting
Users can create new models for their particular applications and run models in an operational setting.
Short-Term Forecasting of CO2 Emission Intensity in Power Grids by Machine Learning
The analysis was done on data set comprised of a large number (473) of explanatory variables such as power production, demand, import, weather conditions etc.
