• Rohan Naik

Classical electrical distribution systems have been used to transport electrical energy generated at a central power plant by increasing voltage levels and then deliver it to the end users by gradually reducing voltage level. Traditional whole building energy modeling suffers from several factors, including the large number of inputs, required for building characterization, simplifying assumptions and the gap between the as-designed and as-built building. Prior work has attempted to mitigate these problems by using sensor based machine learning approaches to statistically model energy consumption, applying the techniques primarily to commercial building data, which makes use of hourly consumption of data. It is unclear however, whether these techniques can translate to residential buildings since the energy usage patterns may vary significantly. Until now, most residential modeling research only had access to monthly electrical consumption data. This application will offer opportunities to progress within a layout by providing many facilities and work to be done in the operation of the distribution network that is not limited to the energy supply and demand balance, but to ensure providing the quality criteria of energy and energy measurement. One of the biggest challenge for this application scenarios will be handling the massive amount of data that is expected to be collected from various sources and treated to optimize its operation. In this respect, different machine learning techniques such as artificial neural networks, fuzzy systems, evolutionary programming and other artificial intelligence methods and their hybrid combinations can significantly contribute to solve problems. A machine learning technique, Least Square Support Vector Machines method works best in this domain. It establishes performance for predicting hourly residential consumption. Work shows that Least Square Support Vector Machine is the best predictor.

"Electricity Bill Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.104-107, May-2019, Available :http://www.jetir.org/papers/JETIR1905118.pdf

"Electricity Bill Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp104-107, May-2019, Available at : http://www.jetir.org/papers/JETIR1905118.pdf