Characterisation of thermal energy dynamics of residential buildings with scarce data

Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics

“…However, this metric may not be sufficiently robust and sensitive. More advanced approaches, such as Motif Discovery 20 and Hidden Markov models 21 , can be employed for more precise pattern recognition, particularly when changes are subtle.…”

Ten questions concerning energy flexibility in buildings

“…The Flexibility Function quantifies the response of the controlled system to a specific incentive variation (e. g., electricity price) and is suited for data-driven applications where only the incentive signals and the energy consumption profiles are available (e.g., as shown in Ref. [33]). Additionally [15], proposed a Flexibility Index, which assesses the benefits of using energy flexibility given a specific incentive signal that could come from the grid to motivate a response.…”

A data-driven framework for characterising building archetypes: A mixed effects modelling approach

“…Often, the building models used are based on previously identified archetypes, which are deterministic models that disregard the variability of the heat consumption inside each category. However, buildings that are grouped based on qualitative or quantitative characteristics, such as their usage or year of construction, present significantly different heat responses [5,6]. The causes behind these differences in energy use inside a building can be difficult to identify.…”