Smart buildings with legacy equipment: A case study on energy savings and cost reduction through an IoT platform in Ireland and Greece

Abstract

The increasing need for energy efficiency and sustainability as made reducing energy consumption in buildings a top priority. Information and communication technologies (ICT) have been proven to be an effective tool in achieving this goal. However, many legacy appliances in commercial and residential buildings are energy-intensive and not connected, making their replacement with new, more efficient models a significant upfront cost. In this paper, a solution is presented that integrates legacy equipment such as heating, ventilation, and air conditioning (HVAC) systems and other services into a new internet of things (IoT) platform that can integrate machine learning (ML) algorithms to identify the most effective way of achieving a variety of low-energy goals. Through this platform, features such as flexibility services are enabled, allowing energy cost reduction and other beneficial results. The solution has been tested in real demonstration sites in Ireland, and Greece, considering the specific challenges in each country. The Irish demonstration sites showcased the ability to manage a water heater in a residential building and a CHP unit in a commercial one, resulting in energy consumption reductions of up to 39% and 61%, respectively. In the Greek demonstration sites, the implemented solution in the residential building was able to reduce energy consumption by up to 86% during peak hours, and up to 60% overall, offering a 10% reduction in their monthly bill. In the commercial building the services provided an average of 6.9 kWh (or 4.6%) of energy savings per day leading to a 22% reduction in their monthly electricity bill.

Keywords

• Machine learning
• Smart buildings
• Legacy equipment
• Energy consumption reduction
• Internet of things