Optimizing Building Energy Consumption using Smart Grid Data

This research discusses how data analytics techniques can be applied in the enhancement of building energy management. It is therefore with the purpose of stimulating the right utilization of the practically inexhaustible quantity of data obtained through smart grids that this research seeks to design original approaches to energy management, building performance, and counteracting of negative impacts on the environment. Since Apache spark process is very efficient we do an excellent job in pre-processing and analyzing the data collected. Combining high-level artificial intelligence decision making tools like linear regression as well as the random forest, the relationship was established between energy utilization and determinants, forecast patterns and create accurate prognosis models. In order to present the results in a format that can be easily understood with the help of Tableau to build a data visualization where depending on the values chosen by the stakeholders further steps can be taken in achieving a more efficient use of energy and less expenditure. In our work, we strive to generate knowledge that will support improved energy efficiency of the built environment, and therefore a more sustainable future. The implications of our work have the ability to affect the processes within and energy usage of buildings, saving energies and making cities greener. Furthermore, our work can help with the creation of further reference points for future research in the field of building energy optimization.