Abstract:
The research to monitor the variations of electricity usage in the sector of households is
becoming extremely popular among the demand side management (DSM). One popular
approach is the load profiling of electricity consumption data, recorded on different granularities.
With passage of time, data is becoming more sophisticated due to its high resolution. It’s not just
the data but the algorithms are more efficient these days. With the help of this high-resolution
data and powerful analytical algorithms, researchers are predicting the patterns and behaviors of
electricity consumption in a household with high accuracy. One popular approach these days is
the use of neural nets to observe these patterns of electricity usage. Although neural nets and their
variations are very powerful to learn all the patterns on a temporal level, still the electricity
consumption is very dynamic to observe. There are many factors that contribute and affect the
usage of electricity by individuals in a household.
To solve this issue, and to make our algorithms more accurate in prediction, not just the
electricity usage but also the other factors and constraints are important to consider. Our
algorithms take the electricity consumption data and incorporate it with other factors including
the socio-economic parameters. On this sophisticated and curated data, we applied different
neural nets to observe the usage of appliances in a household. We took different datasets and
applied these techniques with and without the socio-economic parameters and compared the
results. Our research can help to improve and understand the constraints and limitations of
demand side flexibility in the residential sector
