Authors - Ekanand Mungra, Roopesh Kevin Sungkur Abstract - Today's increasing energy demand, particularly in developing regions, supports both economic growth and the improvement of living conditions. However, these regions experience power outages frequently, due to the high energy consumption of commercial buildings. This research examines energy usage in smart commercial buildings by analyzing data from in-building sensors, collected at ten-minute intervals for more than four months. The aim is to forecast the consumption of energy of these buildings while utilizing AI generated scenarios to generate simulations resembling real-life energy usage situations, thereby improving our model’s predictions. In the era of smart buildings, accurate predicting energy usage does not only facilitate cost savings for businesses, but it also presents an opportunity for revenue generation, particularly through the surplus energy supplied back to the grid from renewable sources such as solar panels. Unlike conventional approaches, this research employs MLPRegressor, a sophisticated model, to analyze and predict intricate patterns of energy usage from the sensor data. This research is particularly significant for advancing energy management strategies in commercial sectors of developing countries, promoting energy independence and efficiency.
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