A Comprehensive Technical Analysis of Retrofitting a Danish Residential Area into a Positive Energy District

Aligned with Denmark’s growing smart energy networks encompassing district heating and electricity grids and in harmony with the country’s ambitious energy and environmental objectives, this study aims to elevate the investigation from individual buildings to consider districts and cities. In pursuit of this objective, a case study of an Odense district in Denmark was considered for modeling, simulation, and energy performance improvement. The selected residential area was modeled using City Energy Analyst, an open-source urban scale modeling tool, accounting for different building attributes as well as the particulars of energy networks and supply systems. The validation of a baseline scenario is predicated upon real-world data collected on-site to serve as the benchmark for exploring and evaluating various scenarios and measures for enhancing energy efficiency. Consequentially, eight distinct energy enhancement packages were designed, modeled, and simulated individually. The results were analyzed, and it was shown that an energy improvement package consisting of retrofitting buildings’ constructions, indoor thermal comfort setpoint management, heating system upgrade, photovoltaic-thermal unit installation, and heat pump integration, along with a seasonal energy storage system, is capable of enhancing the overall energy efficiency quotient of the area, establishing a positive energy district with an excess in heat and electricity production

Fault detection and diagnostics in ventilation units using linear regression virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are one of the largest components in buildings systems and are responsible for large part of energy consumption. Ventilation units are complex components, often customized for the specific building. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper Fault Detection and Diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose to exploit physical relations inside the unit to create virtual sensors from other sensors' readings, introducing redundancy in the system. We create linear regression models for three sensors using other sensors related through physical laws as inputs. We use two different measures to detect when a virtual sensor deviates from the actual one: coefficient of determination and acceptable range. We test our method on a real building at the University of Southern Denmark. Our method detects a fault in temperature sensor, where its readings have an abnormal trend and fall outside acceptable ranae for one day.Postprint (author's final draft

A method for fault detection and diagnostics in ventilation units using virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.Peer ReviewedPostprint (published version

The trade-off between deep energy retrofit and improving building intelligence in a university building

In the last three decades, deep energy retrofit measures have been the standard option to improve the existing Danish building stock performance, with conventional techniques including envelope constructions insulation, windows change and lights replacement. While such techniques have demonstrated large technical and economic benefits, they may not be the optimal solution for every building retrofit case. With the advancement in the field of smart buildings and building automation systems, new energy performance improvement measures have emerged aiming to enhance the building intelligence quotient. In this paper, a technical evaluation and assessment of the trade-off between implementing deep energy retrofit techniques and improving building intelligence measures is provided. The assessment is driven by energy simulations of a detailed dynamic energy performance model developed in EnergyPlus. A 2500 m2 university building in Denmark is considered as a case study, where a holistic energy model was developed and calibrated using actual data. Different performance improvement measures are implemented and assessed. Standard deep energy retrofit measures are considered, where the building intelligence improvement measures are in compliance with the European Standard EN 15232 recommendations. The overall assessment and evaluation results will serve as recommendations aiding the decision to retrofit the building and improve the performance

Dynamic Energy Modelling as an Alternative Approach for Reducing Performance Gaps in Retrofitted Schools in Denmark

When considering that over 80% of buildings in Denmark were built before the 1980′s, a holistic energy retrofitting of the existing building stock is a major milestone to attain the energy and environmental targets of the country. In this work, a case study of three public schools is considered for post-retrofit process evaluation. The three schools were heavily retrofitted by September 2018 with energy conservation and improvement measures that were implemented targeting both the building envelope and various energy systems. A technical evaluation of the energy retrofit process in the schools was carried out, when considering one year of operation after the completion of the retrofitting work. Actual data from the heating and electricity meters in the schools were collected and compared with the pre-retrofit design numbers which rely majorly on static tabulated numbers for savings evaluation. It was shown that the retrofit design numbers largely overestimate the attained savings, where the average performance gap between the expected and real numbers for the three schools is around 61% and 136% for annual heating and electricity savings, respectively. On the other hand, an alternative approach was proposed where calibrated dynamic energy performance models, which were developed for the three schools in EnergyPlus, were used to simulate the impact of implementing the retrofit measures. It was shown that implementing this approach could predict much better the impacts of the retrofit process with an average gap of around 17% for heating savings and 21% for electricity savings. Based on the post-retrofit process evaluation in the three schools, it was concluded that using dynamic model simulations has the potential of lowering the performance gap between the promised and real savings when compared to static tabulated approaches, although the savings are still generally over-estimated in both approaches

Online Energy Performance Monitoring and Evaluation for Continuous Commissioning in a Danish Office Building

The development, implementation, and evaluation of an online building energy performance monitoring and evaluation ‘ObepME’ platform in a 2600 m2 Danish office building is presented and discussed. A whole building dynamic energy model was developed in EnergyPlus and calibrated using actual onsite data. The calibrated model is then used as a basis for continuous and automated commissioning. A list of performance tests was developed targeting energy consumption on different levels. An online dashboard was created to automatically compare and visualize model simulations and actual building consumption as part of the building continuous commissioning. The model development and calibration along with ObepME implementation are presented in this paper. Major findings from the continuous commissioning platform and examples of malfunctions observed are reported and analysed