A commercial real-estate owner used machine learning algorithm to reduce energy consumption by ~1.5%

The commercial real estate client owns multiple assets in Japan. They had invested in IoT based monitoring of HVAC system in their building. And were now looking to utilize advanced analytics based approach to map user preferences and optimize energy consumption in the building.

The client was looking for an advanced analytics based decision system to overlay on top of temperature, humidity and external weather data to optimize the energy consumption.

Smart system for cost efficiency

Superimposing temperature and weather data from local weather station, we are able to identify ideal settings and optimized setting for the HVAC system.

• Recommended temp. setting would reduce the power consumption by 1.5%
• 1⁰ incremental temp. differential increases power consumption by 1.2%
• Single AC On/Off cycle results in ~1.6 units of additional power consumption

The model had to be trained on large volume of consumption data and the results were generated using 6-month IoT data through a retrospective study.

Multiple dependencies. Complex modeling.

The real estate client has deployed sensors and IoT based monitoring of temperature and humidity for one of their commercial assets. They wanted to understand how IoT data and a machine learning based decision system could help them optimize the power consumption and reduce manual operations.

One of the key gaps we identified based on the data being Collected was non-availability of occupancy data. In the absence of this data, power wastage could not be computed.

Another issue with the data being collected through IoT system was lack of integration with an outside weather data source. The third issue identified was with the data quality itself – having AC temperature observations that were practically not possible. We proceeded with a stochastic non-linear model to identify efficiency issues.

State of the art ML approach optimizes energy consumption

The study was taken up as a pilot to assess the potential of machine earning driven decision system to drive energy efficiency in smart buildings.  

We took up a retrospective study to analyse the energy wastage over a 6-month period and demonstrate the insights from an ML driven decision system. We were able to identify the data gaps, and also make recommendations, including:

a. The optimal temperature to be maintained in a room based on outside temperature

b. Ideal temp. readjustment frequency – as it lead to additional power consumption

The real power of Machine Learning based HVAC load management system can be realised if it is integrated with the IoT platform