Building a predictive model for occupancy and energy usage usage using Machine Learning

To build a predictive model using occupancy and energy usage data to forecast expected occupancy and energy availability (kWh) to off-load to the grid.The system to be developed will use historical occupancy data as well as energy utilization data over a time period to build a model to predict future usage. The usage data will be compared to actual available energy as per design occupancy and ventilation load. The delta between the design mode and the actual usage will determine the available energy that can be sold back to the grid.

Artificial Intelligence & Machine Learning Application

Our company deals with energy management based on space occupancy. The occupancy data is used towards controlling ventilation in any space. We are looking at developing an AI/ML model to predict how a space is expected to be occupied and what would be the needed ventilation requirement for the monitored space.We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to any of the aforementioned applications.This will involve several different steps for the students, including:Conducting background research on our existing products and the dataset.Analyzing our current dataset.Researching the latest AI / ML techniques and how they could be applied to our data.Developing an AI / ML model that provides unique outcomes or insights into our data.Providing multiple solutions that can be applied to solve the same problem.

Energy Analysis Modeling - Building Automation

The objective of building the 'Energy Model Analytics' is to gather critical data about a building and to understand how it is currently being ventilated and managed with respect to its HVAC (Heating, Ventilation, and Air Conditioning) function. This data will help us identify potential energy-saving and GHG(Green House Gas) reduction opportunities.There are two phases of data collection, the first phase will involve the collection of building-specific details to evaluate if the building will be a potential site for energy and GHG reduction. The second phase will be to collect detailed information about the functioning of the HVAC system to calculate the energy savings. The collected data will be used to calculate estimated electrical and thermal savings.The key metric used to calculate the savings is driven by occupancy data, which is the core of our solution to control the ventilation in a building. The baseline calculation will calculate the current energy usage (electrical and thermal) based on the current operation of the HVAC system.