Abstract:
This project aims to develop an accurate power forecast of a photovoltaic (PV) system for active
buildings, using machine learning (ML) models. The objective is to create a demand response
system that regulates energy usage in active buildings by providing recommendations for
efficient power usage to the user.
In this project, we have collected historical data on environmental factors such as solar irradiance
and compared various ML models to select the best one based on its accuracy. The selected ML
model is implemented to generate the power forecast of the PV system. To create the demand
response system, a mobile application is developed that uses the power forecast to provide
recommendations for efficient power usage to the user.
Our results show that the ML model accurately predicts the power output of the PV system and
that the demand response system based on the power forecast can help to manage energy
consumption in active buildings. This project demonstrates the potential of ML-based power
forecasting and demand response systems in achieving sustainable and energy-efficient
buildings.
