Executive Summary : | With the use of information and communication technologies (ICT), building processes and management have been automated, resulting in a new radical shift known as Smart Buildings, that can enhance the customer's comfort and productivity while using less energy than that of a typical building. Smart buildings may also communicate with the electric grid, which is becoming highly significant for utilities load management systems that require exact forecasts of smart building power usage. The ability to estimate electricity usage in a building is crucial for identifying energy-saving opportunities as part of the automation of the building design. Buildings must be more adaptive and robust while requiring less electricity and preserving customer comfort, which helps to alleviate the impacts of climate change. Peak energy consumption can be predicted using historical data and information, allowing consumers to instantly manage their energy use while also delivering a load-side management response approach to utilities for real-time control and actuation. In view of this, the proposal highlights the usefulness of data-driven approaches in forecasting the electricity demand of a smart building in a model-free environment. Building management systems (BMS) are vital for the effective management of smart building architecture, technology, and subsystems. It serves as the smart building core monitoring and operating entity. The most apparent flaw in this is that BMS fails to incorporate data management, analytics, computing, and control systems for complicated settings. Flexibility, multi-sensor integration, predictive analysis, and dynamic optimization are only a few of the features and functions that are lacking. As a result, in this proposal, the artificial intelligence (AI) field, big data analytics, and various data-driven approaches introduce new methodologies for the development of intelligent BMSs to construct valuable information such as occupancy behavior, fault or weather forecasting, energy consumption patterns, etc. to address user comfort while ensuring maximum efficiency and minimizing carbon footprints. |