Akademik Veri Yönetim Sistemi
10th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL '19), Jinan, Çin, 6 Kasım - 08 Aralık 2019, ss.25-35
Both
appliance ownership and characteristics determine residential electricity
consumption, the exact electricity consumption of each appliance depends on the
usage patterns of the occupants. Various approaches have been used to determine
the appliance specific electricity consumption at household level. One of the
statistical approaches used since 1980s is the conditional demand analysis
(CDA) which takes into account appliance ownership, appliance characteristics
(such as volume, size, power, etc.), weather, and market data to disintegrate
the billing data into appliance specific form. Since 1990s, neural network and
fuzzy logic concepts which are artificial intelligence-based modelling
approaches have been used to determine the appliance specific electricity
consumption using various types of data available on appliance and occupant
characteristics. An artificial intelligence-based approach which combines the
prediction performance capabilities of neural networks and fuzzy logic is
adaptive network based fuzzy inference system (ANFIS) models have been used
such as for the water quality, hourly electricity load demand, and air
pollutant emission estimation studies. According to the investigations and
researches, it has been observed that ANFIS approach has not been used to model
end-use electricity consumption of the residential sector, yet. The aim of this
study is to compare the prediction performances of CDA based and ANFIS based
approaches to determine the electricity consumption based on the appliances at
household level. An extended survey data which covers detailed information
about 92 different types of appliances including all domestic and minor
appliance properties, occupant characteristics, and billing information of 260
homes is used for developing the CDA and ANFIS models. It has been found that
while CDA model is developed with the mean absolute percent error (MAPE) of 12%, ANFIS model has been
finalized with the mean absolute percent error (MAPE) of the testing data as 17%.
Besides the MAPE values, based on the error distribution graphs of each
approach, it has been observed that CDA model has significantly high prediction
performance than ANFIS approach for the appliance-based electricity consumption
estimation. As a last step of the study, different scenarios have been applied
to estimate the appliance impacts on household electricity consumption.
Scenarios are applied by considering the usage patterns of lighting, dishwasher
and washing machine and percent decrease values in overall electricity consumption
are observed as 0.7%, 4.3%,1.4% respectively.