Electricity bills must be some of the scariest letters to the average citizen. The cost of energy remains one of the major costs for most households, adjusted by factors ranging from taxes to the weather. At least until renewable energy increases efficiency or the first viable commercial fusion reactors appear. Of course, in addition to the cost of energy, there are also environmental issues to address. The current focus is on efficiency in automobiles and household appliances. Fuel-efficient vehicles that achieve 40MPG, low-voltage light bulbs…and now, smart buildingThe Internet of Things (IoT) has been all the rage in the last few years, but the time is ripe for another similar concept, the Internet of Energy.
Following on from our previous article on biomimicry, which featured pine cones as a source of inspiration for improving building cooling, we describe two software-based techniques designed to improve the energy efficiency of buildings.
Energy IN TIME, an integrated approach for smart buildings
In most countries, the daily operation of buildings uses at least 40% of the energy consumed. And considering the life of a building, from the moment the first foundation is laid until final decommissioning, 90% of the carbon footprint occurs during the post-construction operational phase. And that’s mainly due to HVAC (heating, ventilation and air conditioning), lighting and electrical systems. Finding an integrated approach to managing all these resources and optimizing it by introducing automation and remote control technology is the goal of Energy IN TIME (EiT). This is a project supported by the EU, led by ACCIONA and with several partners such as CIRCE. Cork Institute of Technology (Ireland) or the Universities of Granada (Spain) and Lorraine (France).
This project is based on a software simulation that will later be implemented in a non-residential building electrical system with centralized control technology. EiT is designed as a 9-step process. First, by calculating Requirements and system architecture, and data collection. afterwards, simulation reference model It was introduced to establish the behavior of buildings under different energy consumption scenarios.After that, the development of the entire building intelligent control system, the core technology that integrates algorithms that measure occupancy and weather conditions. In the next stage Diagnostic and analytical system Collected data and its Integration and verification. Finally, the actual demonstration and exploitation of technology.
The project is currently being tested and validated in four European non-residential cities (France, Romania and Finland) under different climatic conditions, together with constructive quality, usage and age situations.
The goal is to achieve substantial optimization building energy efficiency It features innovative management and control solutions that achieve up to 20% energy savings over traditional energy networks and their operating and maintenance costs.
Real-time measurement of building energy footprint
What if you could always know the detailed energy consumption of your building? And what information could you send to a smart network that could take advantage of off-peak hours and allocate resources at all times? Federal Institute of Technology Lausanne (EPFL) A research team from the Electronics Laboratory (ELab) in , just published their results in the Elsevier Automation in Construction journal.
There are now several smart devices that can measure your arrival and automatically turn on the lights and heat in your home, or program your coffee machine. The goal of this research is to develop software that can integrate all the data provided by various sensors, including those integrated in wearables, and transmit them to smart networks.
This Swiss team’s first approach could be a turning point in realizing this kind of software. This enables two-way communication between the building and the network, making it possible to harness the full potential of the Internet of Energy combined with smart buildings. , smart grids, and storage systems.
This kind of information is particularly useful for managing renewable energy, which is extremely variable due to variations in wind and solar cycle speeds. For example, the system predicts a sharp increase in electricity demand and supply and turns on the heating of the house before the programmed time. Similarly, if there is a strong demand on the network, the home temperature can be lowered within a reasonable user-defined range.
The team is already working smart building A simulator that can handle all conditions Optimize energy consumptionThe challenge is optimizing the flow of information between sensors and the smart grid. This could potentially cover an entire city.
sauce: Physics, EPFL, energy of time
