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Minimizing the Operating Cost of Buildings Using Artificial Intelligence
About The Project Faculty Members Students Industry Partner

Goals

Big companies and institutions that operate a significant number of facilities are always looking for new ways to optimize their energy consumption, which also correlates with long-term financial savings. Most modern and renovated buildings are equipped with sensors and meters for heating, ventilation, and air conditioning (HVAC) systems as well as for electricity and water systems. We aim to optimize energy consumption by using the data generated from these systems to mine patterns and predict the operation conditions of each system.

 

In terms of financial operating efficiency, the fixed costs and additional costs of operation need to be calculated. The aim will be to minimize all additional costs while maintaining the fixed, baseline costs as required. Decision-making will be based on the expected demand on the various systems (mentioned above) at different times. A monitoring system should map the usage of and demand on these systems in different parts of the buildings to create expectations and keep track of changes.

 

Besides the financial benefits, we aim to increase customer satisfaction. This will be achieved through detecting the occupancy on each level of each building, hourly calibrating the temperature depending on the weather forecasts, and providing the ideal temperature and the proper amounts of filtered air.

Challenges

Challenges

A lot of data is being generated from the sensors and meters in buildings. Monitoring and evaluating this data is being done manually, and the responsible supervisors have to detect anomalies. Unfortunately, it is not possible for the supervisors to monitor the systems around the clock for abnormal consumption in different systems because of the large data generated every hour. This also means that they are not able to detect mechanical and electrical problems in the system. That is most often the case when rooms and/or buildings have low or no occupancy and the systems are still functioning. This leads to a huge energy loss and therefore, high operational costs and poorly maintained systems. Most of the already existing techniques focus on the collection of data from sensors, where they focus on the use of space, water, usage, and allocation of energy. In addition, users are usually unsatisfied with the temperature in buildings because it is usually set manually without considering the hourly changes of the outside temperature.
Desired Disciplines
  • Machine learning
  • Programming
  • Data Science
  • Internet Of Things
  • Data Management and Visualization
  • Software engineering
  • Cybersecurity

Faculty Members

Students

Sarah Sandakli

Economics

Malaz Tamim

Computer Science

Ghumdan Al-Sabahi

Computer Science

Elie Rizk

Computer Science

Majd Al Kawas

Computer Science

Industry Partner

Beirut Digital District (BDD)

Beirut Digital District (BDD)

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