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

• Published in: Results in engineering Vol. 22; p. 102095
• Main Authors: Ntafalias, Aristotelis, Papadopoulos, Panagiotis, Ramallo-González, Alfonso P., Skarmeta-Gómez, Antonio F., Sánchez-Valverde, Juan, Vlachou, Maria C., Marín-Pérez, Rafael, Quesada-Sánchez, Alfredo, Purcell, Fergal, Wright, Stephen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2024; Elsevier
• Subjects: Energy consumption reduction; Internet of things; Legacy equipment; Machine learning; Smart buildings; Internet of things; Smart buildings; Legacy equipment; Machine learning; Energy consumption reduction
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2024.102095

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. •The paper shows an ICT solution which is capable of hosting intelligent algorithms to be used on the management of buildings.•The solution integrates legacy equipment that has been upgraded to make it connectable.•Demand modification techniques adjust demand to reduce peaks and shift to greener hours.•The method has been tested on two different sites with different climates and have given good results.