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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 electrification of transport, has placed a great deal of attention into electrical energy storage. Several battery technologies exist, but all of them are based on banks of modules that connected together add up the voltage of the current needed for each application. The chemical processes that occur within the modules may condition the life of the battery. This points to the direction of management systems that can control how the batteries are used. In this work we suggest an IoT platform that can manage the control of multi-modular battery banks and that can be accessible for the many (MIT license). The platform has been proven to be sound, and it gives multiple possibilities to users and developers. A use case has been shown on this paper in which the platform allows the management of a real battery bank providing real data and applying an intelligent algorithm that forecasts the charging process and controls that the variables are within acceptable ranges.

Since 1997, when the first hybrid vehicle was launched on the market, until today, the number of NIMH batteries that have been discarded due to their obsolescence has not stopped increasing, with an even faster rate more recently due to the progressive disappearance of thermal vehicles on the market. The battery technologies used are mostly NIMH for hybrid vehicles and Li ion for pure electric vehicles, making recycling difficult due to the hazardous materials they contain. For this reason, and with the aim of extending the life of the batteries, even including a second life within electric vehicle applications, this paper describes and evaluates a low-cost system to characterize individual cells of commercial electric vehicle batteries by identifying such abnormally performing cells that are out of use, minimizing regeneration costs in a more sustainable manner. A platform based on the IoT technology is developed, allowing the automation of charging and discharging cycles of each independent cell according to some parameters given by the user, and monitoring the real-time data of such battery cells. A case study based on a commercial Toyota Prius battery is also included in the paper. The results show the suitability of the proposed solution as an alternative way to characterize individual cells for subsequent electric vehicle applications, decreasing operating costs and providing an autonomous, flexible, and reliable system.