Simulation-assisted building energy performance improvement using sensible control decisions

The building sector contributes significantly to global energy consumption and emission of greenhouse gases. Thermal insulation along with installation of energy-efficient building systems can reduce energy needs while preserving or improving occupant comfort levels. Still sensible control decisions, to harmoniously and effectively operate all building thermal systems, can be used to further improve building energy performance and/or thermal comfort. In this article, a simulation-assisted methodology is presented to automatically generate such decisions. There are two ingredients to our approach: a thermal simulation model - - a surrogate of the real building - - used to evaluate the effects of potential decisions; and, a cognitive adaptive optimization algorithm used to intelligently search for the "best" control decision. A user-defined cost function is used to compare various decision strategies. Corroborating simulation results are presented to quantify the expected benefits of the proposed approach. © 2011 ACM

Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition

In this article the most fundamental decomposition-based optimization method - block coordinate search, based on the sequential decomposition of problems in subproblems - and building performance simulation programs are used to reason about a building design process at micro-urban scale and strategies are defined to make the search more efficient. Cyclic overlapping block coordinate search is here considered in its double nature of optimization method and surrogate model (and metaphore) of a sequential design process. Heuristic indicators apt to support the design of search structures suited to that method are developed from building-simulation-assisted computational experiments, aimed to choose the form and position of a small building in a plot. Those indicators link the sharing of structure between subspaces ("commonality") to recursive recombination, measured as freshness of the search wake and novelty of the search moves. The aim of these indicators is to measure the relative effectiveness of decomposition-based design moves and create efficient block searches. Implications of a possible use of these indicators in genetic algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

The Potential of Liquid-Based BIPV/T Systems and Ice Storage for High Performance Housing in Canada

ASHRAE Vision 2020 has defined market viable net-zero energy buildings as a key objective for new construction in North America. Designing for this target requires the effective integration of renewable energy systems into the building. However, many buildings have limited roof and façade areas in which to integrate these systems, making it difficult to achieve a net zero energy design. Building Integrated Photovoltaic and Thermal (BIPV/T) offers a potential solution to this issue by converting the building envelope into an active producer of both thermal and electrical energy. Commonly, BIPV/T systems in North America have used air as a working fluid. While this offers easy integration with the building ventilation system, air also has a lower thermal capacitance, reducing thermal energy extracted from a BIPV/T collector. Liquid based systems offer working fluids with higher thermal capacitance, along with the ability to easily integrate with existing thermal storage systems. However, these systems often circulate warm water in order to directly meet heating and hot water loads, resulting in reduced thermal and electrical efficiencies and less durable BIPV/T modules. Circulating cooler water to the collectors can significantly improve both the thermal and electrical efficiencies of liquid based BIPV/T systems. However, the low grade thermal energy collected must then be upgraded for use within the building. This paper examines the potential of using liquid based BIPV/T systems with cool storage and heat pump technologies to meet the thermal demands of a high performance Canadian home. An innovative liquid based BIPV/T system is proposed in which the collector array is connected to a cool storage tank, while a heat pump is used to upgrade and deliver thermal energy to the building. Both sensible and ice-based latent storage options are examined as cool storage possibilities. To perform the analysis, TRNSYS is used to simulate the proposed system integrated into a high performance home in Montreal, Canada. Annual simulation results are presented and compared with typical base case designs. A more detailed temporal analysis of electrical loads is also performed in order to examine the impact of the proposed system on the electricity grid.

Technological options and strategies towards zero energy buildings contributing to climate change mitigation: A systematic review

A systematic review of the technological options and strategies to achieve zero energy buildings was carried out to establish today state-of-the-art knowledge base and to present key design and performance factors that define those technologies with the final aim of contributing to climate change mitigation options of buildings. All relevant literature published from January 2013 to August 2019 was critically as- sessed. A total of 14,895 papers were identified and 220 reviews were evaluated as first literature source; this literature showed that the published information is diverse and not organized, therefore climates and building typologies is not possible solely through published information. Collected evidence shows that with appropriate design, buildings can contribute to climate change mitigation decreasing the embodied energy in the materials used in their construction and decreasing the energy demand and use during their operation phase.This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE). This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme

Evaluation and improvement of energy flexibility and performance of building heating, ventilation, and air-conditioning systems

The foreseen reduction of available fossil fuels, the continued increase in global energy demand, and the irrefutable evidence of climate change, along with the implementation of a global commitment to achieve a net-zero emissions target, have greatly sharpened commercial interest in using renewable energy resources (RER). However, the high penetration of RER-based stochastic power generation systems has resulted in a significant requirement for increased flexibility on the demand side that can allow buildings to adapt to increasingly dynamic energy supply conditions to support power grid operation and optimization. Failure to adapt may carry serious electrical blackouts and can compromise the safety of the supply side. The building sector accounts for a substantial amount of global energy usage and offers great opportunities for energy flexibility. Building energy flexibility is an important and emerging concept in the modern energy landscape, which can support the sustainable transition of the power sector. Building heating, ventilation, and air-conditioning (HVAC) systems are one of the leading energy consumers in buildings, which can be used as a key flexible source. The HVAC systems with integrated thermal energy storage (TES) can further enhance building energy flexibility. This thesis contributes to the evolving field of demand flexibility and introduces methodologies to evaluate and improve energy flexibility and performance of building HVAC systems

Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.Research leading to these results has been partially supported by the Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 680517. Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission H2020-EeB5-2015 project “Optimised Energy Efficient Design Platform for Refurbishment at District Level” under Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded by MOEEBIUS project. This paper reflects only the authors’ views and the Commission is not responsible for any use that may be made of the information contained therein