Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

Extracting demand-side flexibility is important to utilize renewable energy as much as possible with keeping the grid stable. Heating, ventilation, and air conditioning (HVAC) system takes a necessary role in our comfort life and consumes more than 40% energy consumption of typical office buildings. HVAC systems are known as potential resources to extract demand-side flexibility because of easy performance control. This paper proposes a comfort-aware HVAC aggregation method based on deep reinforcement learning to both maintain a comfortable temperature and extract flexibility. Experiments show the proposed method satisfies the given demand-response requirements and thermal comfort.

This paper proposes a thermal comfort-aware demand-side flexibility-oriented Heating, Ventilation, and Air Conditioning (HVAC) aggregation method. It aims to balance maximizing occupants’ thermal comfort with generating demand-side flexibility by adjusting HVAC temperature setpoints. The approach involves formulating the problem through Linear Programming (LP) to optimize HVAC operation schedules while considering thermal comfort. Our simulation results demonstrate that our approach significantly generates the flexibility while maintaining thermal comfort among users.

Non-Intrusive Load Monitoring (NILM), which provides sufficient load information from the energy consumption of the entire building, has become crucial in improving the operation of energy systems. Although it can decompose overall energy consumption into individual electrical sub-loads, it struggles to identify such thermal-driven sub-loads as occupants. This paper explores and proposes a Non-Intrusive Thermal Load Monitoring (NITLM) with recursive models and input data selection to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. In experiments, we generated a thermal load dataset derived from a whole building energy simulation and compared the accuracy of the monitoring results with the generated reference data. Our experimental results show that our designed model reduces MAE by up to 77.0% more than the existing NITLM approach.