![]() Finally, research shows that cross-domain information, e.g. Additionally, building energy simulation can be used as a virtual testbed for the evaluation of individual and grid level operational optimisation and demand response strategies. Building energy simulation (BES) can support these analytics-based solutions through baseline modelling for energy-saving estimations in M&V, enabling quantitative model-based AFFD, and model-based PdM. ![]() Analytics solutions include monitoring-based commissioning, Automated Fault Detection and Diagnostics (AFDD), Predictive Maintenance (PdM), Measurement and Verification (M&V), operational optimisation, demand response electricity supply, among others. Lawrence Berkeley National Laboratory (LBNL) documented energy analytics enabled primary energy savings ranging from 12 to 30%. It has been shownthat adequate hardware across the building, including submetering, sensors, actuators, and integrated analytics and control software, can save energy by influencing user behaviour, operations optimisation and uncovering inefficiencies onlydetected when combining multiple data sources. The country has set the goal of achieving an 80% reduction in greenhouse gas (GHG) emissions by 2050 against a 1990 baseline. In the UK, buildingrelated industries accounted for 20% of the total annual greenhouse gas emissions in 2018. I INTRODUCTION In 2017, building stock accounted for 36% of the energy used globally and was responsible for 39% of the total CO2 emissions.
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