Abstract
The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.
Highlights
Buildings can account for a high percent of the energy consumption of the society
To fill the above research gap, a cooling load Non-Intrusive Load Monitoring (NILM) method is proposed in this study to disaggregate the cooling load into different categories based on artificial neural networks (ANNs)
The hourly total cooling load, building envelope load, occupant load, equipment load and fresh air load are collected as samples for the NILM method
Summary
Buildings can account for a high percent of the energy consumption of the society. Efficient management of building energy systems can reduce the energy consumption of buildings significantly. The NILM technique employs intelligent algorithms to disaggregate the total energy consumption collected by a smart meter into individual appliances and identify the loads without adding intrusive sensors [2]. To fill the above research gap, a cooling load NILM method is proposed in this study to disaggregate the cooling load into different categories based on artificial neural networks (ANNs). On the load source and characteristics, the total cooling load for the metro station can be separated into four types: the load from building envelope, the load from fresh air (including mechanical air supply systems and infiltration trough the entrances), the load from occupants (passengers getting on and off the train) and the load from the equipment (lighting, elevator, LED screen, etc.).
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