Electricity consumption pattern disaggregation based on user utilization factor

Non-Intrusive Appliance Load Monitoring (NIALM) technique has been studied intensively by many researchers to estimate the electricity consumption of each appliance in a monitored building. However, the method requires a detailed, secondby- second power consumption data which is commonly not available without the use of high specification energy meter. The common energy meter used in buildings can only capture low frequency data such as kWh for every thirty minutes. This thesis proposes a bottom-up approach for disaggregating kWh consumption of a building. The relationship between the load profile of a building and electricity usage pattern of the occupants were studied and analysed. From the findings, a method based on utilization factor that relates user usage pattern and kWh electricity consumption was proposed to perform load disaggregation. The method was applied on the practical kWh profile data of electricity consumption of Block P19a, Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia. The disaggregated kWh consumption results for air-conditioning and lighting system were validated with the actual kWh consumption recorded at the respective branch circuits of the building. Results from the analysis showed that the proposed method can be used to disaggregate energy consumption of a commercial building into air-conditioning and lighting systems. The proposed method could be extended to disaggregate the energy consumption for different areas of the building

Electricity consumption pattern disaggregation using non-intrusive appliance load monitoring method

In practice, a standard energy meter can only capture the overall electricity consumption and estimating electricity consumption pattern of various appliances from the overall consumption pattern is complicated. Therefore, the Non-Intrusive Appliance Load Monitoring (NIALM) technique can be applied to trace electricity consumption from each appliance in a monitored building. However, the method requires a detailed, second-by-second power consumption data which is commonly not available without the use of high specification energy meter. Hence, this paper analyzes the impact of different time sampling data in estimating the energy consumption pattern of various appliances through NIALM method. This is so that consumers will have an overview of time sampling data which is required in order to apply the NIALM technique. As for the analysis, air-conditioning systems and fluorescent lamps were used in the experimental setup. One minute sample rate was the minimum time interval required by NIALM carried out in this analysis. Through the study presented in this paper, it can be established that higher time sampling led to uncertain appliance detection and low accuracy

Disaggregated Electricity Bill Base on Utilization factor and Time-of-use (ToU) Tariff

Time of Use tariff is introduced to motivate users to change their electricity usage pattern. Commonly the tariff is high during peak hours and relatively low during off peak hours, to encourage users to reduce consumption during peak hours or shift it to off-peak hours. This tariff scheme provides opportunities for building owners to reduce their electricity bill provided that their electricity usage patterns of various spaces in that building at every hour are known. In practice, the kWh meter installed by the utility can only provide the overall hourly electricity consumption pattern. To know the usage pattern of different spaces or rooms, separate individual meter need to be installed in each space/room, which is costly and impractical.  This paper presented the disaggregated electricity bill method based on user utilization factor and time of use (ToU) tariff. It estimates hourly electricity bill of each appliance at each space/room. Utilization factor is used to represent the electricity usage behavior of the occupants. The proposed method is applied on practical load profile data of a university building