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Found 7 papers, 1 papers with code
Creating synthetic energy meter data using conditional diffusion and building metadata
Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata.
The Forecastability of Underlying Building Electricity Demand from Time Series Data
Forecasting building energy consumption has become a promising solution in Building Energy Management Systems for energy saving and optimization.
On the contribution of pre-trained models to accuracy and utility in modeling distributed energy resources
Despite their growing popularity, data-driven models of real-world dynamical systems require lots of data.
Is your forecaster smarter than an energy engineer: a deep dive into electricity price forecasting
The field of electricity price forecasting has seen significant advances in the last years, including the development of new, more accurate forecast models.
Deep Reinforcement Learning for Optimal Control of Space Heating
Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e. g. existence of building models.
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings
Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort.
Deep Reinforcement Learning based Optimal Control of Hot Water Systems
Energy consumption for hot water production is a major draw in high efficiency buildings.
