1. Visual Comfort and Energy Efficiency for User Centric Lighting Control
Energy consumption for lighting constitutes a sizable portion of the overall energy consumption of commercial office buildings. Many smart lighting control products are already available in the market, but their penetration has been limited and even installed systems have had limited use. One of the main reasons is that they tend to control lighting based on universal set-points which are agnostic to the individual preferences of the occupants thus hampering their comfort. The paper will present an automated lighting control framework which dynamically learns the lighting preferences of each user, models his visual comfort and controls the light dimming in a truly personalized manner so as to always control the comfort vs. energy efficiency trade-off. This approach effectively removes the single most important complaint of occupants when using such systems, loss of comfort, and paves the way for their wide scale adoption in order to untap the energy reduction potential of commercial lighting.
Building and district modelling (BIM, CityGML…) are key technologies for the deployment of energy efficiency strategies at building and district level, from the initial stages of planning and design to the operation and maintenance ones. These technologies allow satisfying the interoperability requirements that fa-cilitate the cooperation among the multiple stakeholders and provide the framework to develop more intelli-gent tools. This paper introduces five complementary European R&D projects in which TECNALIA is col-laborating, very good examples of innovative systems based on these concepts. MOEEBIUS enhances passive and active building elements modelling approaches enabling improved building energy performance simula-tions.
3. MOEEBIUS energy performance optimization framework in buildings for urban sustainability:
With the increasing demand for more energy efficient buildings, the construction and energy services industries are faced with the challenge to ensure that the energy performance and savings predicted during energy efficiency measures definition is actually achieved during operation. There is, however, significant evidence to suggest that buildings underperform illustrating a, so called, “performance gap” which is attributed to a variety of causal factors related to both predicted and in-use performance, implying that predictions tend to be unrealistically low whilst actual energy performance is usually unnecessarily high. In turn the successful penetration and effective application of ESCO business models relies on minimizing the gap between actual and predicted building energy performance. The aforementioned gap, though, prohibit the scaled deployment of energy efficiency projects constituting a significant barrier to the development of the ESCO market. The overall problem (performance gap) could be basically interpreted as an inability of current modelling techniques to represent the realistic use and operation of buildings. MOEEBIUS H2020 project introduces a Holistic Energy Performance Optimization Framework that enhances current (passive and active building elements) modelling approaches with advanced user behaviour modelling and machine learning technologies to create an innovative suite of end-user tools and applications enabling: (i) accurate Building Energy Performance prediction, (ii) precise allocation of detailed performance contributions between critical building components and operations, (iii) real-time building performance optimization, (iv) optimized retrofitting decision-making and, (v) real-time peak-load management optimization at the district level. Through the provision of a robust technological framework MOEEBIUS will enable the creation of attractive business opportunities for ESCOs, Aggregators, Maintenance and Facility Managers in evolving and highly competitive energy services markets.
4. Multiradio, multiboot capable sensing systems for home area networking:
The development of Wireless Sensor Networking technology to deploy in smart home environments for a variety of applications such as Home Area Networking has been the focus of commercial and academic interest for the last decade. Developers of such systems have not adopted a common standard for communications in such schemes. Many Wireless Sensor Network systems use proprietary systems so interoperability between different devices and systems can be at best difficult with various protocols (standards based and non-standards based) used (ZigBee, EnOcean, MODBUS, KNX, DALI, Powerline, etc.). This work describes the development of a novel low power consumption multiradio system incorporating 32-bit ARM-Cortex microcontroller and multiple radio interfaces - ZigBee/6LoWPAN/Bluetooth LE/868MHz platform. The multiradio sensing system lends itself to interoperability and standardization between the different technologies, which typically make up a heterogeneous network of sensors for both standards based and non-standards based systems. The configurability of the system enables energy savings, and increases the range between single points enabling the implementation of adaptive networking architectures of different configurations. The system described provides a future-proof wireless platform for Home Automation Networks with regards to the network heterogeneity in terms of hardware and protocols defined as being critical for use in the built environment. This system is the first to provide the capability to communicate in the 2.4GHz band as well as the 868MHz band as well as the feature of multiboot capability. A description of the system operation and potential for power savings through the use of such a system is provided. Using such a multiradio, multiboot capable, system can not only allow interoperability across multiple radio platforms in a Home Area Network, but can also increase battery lifetime by 20 – 25% in standard sensing applications.
5. A Platform for Automated Technical Building Management Services Using Ontology:
The deployment of technical building management services is a requirement to further reduce energy demand of future and existing buildings. Automating the process of configuring and deploying technical building management services such as fault detection and diagnosis of technical Equipment seems to be a promising path to intensify the adoption of these services. In this work we present a data processing and analytics execution platform which allows the deployment of ontology-based, automated technical building Management services on a large-scale. We present the platform architecture and results from a reference implementation performing rule-based fault detection on offline air handling unit data.
6. Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort:
Thermostats are widely used in temperature regulation of indoor spaces and have a direct impact on energy use and occupant thermal comfort. Existing guidelines make recommendations for properly selecting set points to reduce energy use, but there is little or no information regarding the actual achieved thermal comfort of the occupants. While dry-bulb air temperature measured at the thermostat location is sometimes a good proxy, there is less understanding of whether thermal comfort targets are actually met. In this direction, we have defined an experimental Simulation protocol involving two office buildings; the buildings have contrasting geometrical and construction characteristics, as well as different building services systems for meeting heating and cooling demands. A parametric analysis is performed for combinations of controlled variables and boundary conditions. In all cases, occupant thermal comfort is estimated using the Fanger index, as defined in ISO 7730. The results of the parametric study suggest that simple bounds on the dry-bulb air temperature are not sufficient to ensure comfort, and in many cases, more detailed considerations taking into account building characteristics, as well as the types of building heating and cooling services are required. The implication is that the calculation or estimation of detailed comfort indices, or even the use of personalised comfort models, is key towards a more human-centric approach to building design and operation.