Screening of energy efficient technologies for industrial buildings' retrofit

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

Effectiveness of CFD simulation for the performance prediction of phase change building boards in the thermal environment control of indoor spaces

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2013 ElsevierThis paper reports on a validation study of CFD models used to predict the effect of PCM clay boards on the control of indoor environments, in ventilated and non-ventilated situations. Unlike multi-zonal models, CFD is important in situations where localised properties are essential such as in buildings with complex and large geometries. The employed phase change model considers temperature/enthalpy hysteresis and varying enthalpy-temperature characteristics to more accurately simulate the phase change behaviour of the PCM boards compared to the standard default modelling approach in the commercial CFD codes. Successful validation was obtained with a mean error of 1.0 K relative to experimental data, and the results show that in addition to providing satisfactory quantitative results, CFD also provides qualitative results which are useful in the effective design of indoor thermal environment control systems utilising PCM. These results include: i) temperature and air flow distribution within the space resulting from the use of PCM boards and different night ventilation rates; ii) the fraction of PCM experiencing phase change and is effective in the control of the indoor thermal environment, enabling optimisation of the location of the boards; and iii) the energy impact of PCM boards and adequate ventilation configurations for effective night charging.This work was funded through sponsorship from the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No: EP/H004181/1

Physical Modelling of a Light Rail HVAC System Using Long-Term Measurements

A method to develop a physical HVAC model of a light rail using only operation data from long-term measurements is presented. Physical HVAC modelling is often based on costly wind tunnel tests or time and computationally intensive CFD calculations. This method is a new approach using only data from everyday operation of the light rail, reducing modelling cost and improving the model accuracy since real life conditions are analysed. Data from two years of passenger operation of a suburban light rail in Karlsruhe, Germany, is used. A standard physical model for a HVAC system and a train compartment is developed. This model is parametrised using data driven modelling and model training. For data driven modelling, the conducted data is analysed and suitable models are derived. For example, the cooling system is modelled using a look-up table based approach developed with the data. For model training, the data is first separated into test and training data. The training data is then separated into different batches (heating up, winter-night, winter-day and summer), to parameterise different physical quantities of the model. Using a systematic grid search, parameters that fit the training data in an optimal manner are found. Finally, the overall model is validated using the test data. Over a large temperature range from -5 °C to 35 °C the model shows good consistency with the test data. The mean absolute percentage error over all test data is 13 %. Within the batches, the summer data was modelled best with an error of about 8 %. The described method allows a fast and reliable method to develop an accurate physical HVAC model

Contrasting the capabilities of building energy performance simulation programs

For the past 50 years, a wide variety of building energy simulation programs have been developed, enhanced and are in use throughout the building energy community. This paper is an overview of a report, which provides up-to-date comparison of the features and capabilities of twenty major building energy simulation programs. The comparison is based on information provided by the program developers in the following categories: general modeling features; zone loads; building envelope and daylighting and solar; infiltration, ventilation and multizone airflow; renewable energy systems; electrical systems and equipment; HVAC systems; HVAC equipment; environmental emissions; economic evaluation; climate data availability, results reporting; validation; and user interface, links to other programs, and availability

Advances to ASHRAE Standard 55 to encourage more effective building practice

ASHRAE Standard 55 has been evolving in recent years to encourage more sustainable building designs and operational practices. A series of changes address issues for which past design practice has been deficient or overly constrained. Some of the changes were enabled by findings from field studies of comfort and energy-efficiency, and others by new developments in the design- and building-management professions.  The changes have been influencing practice and spurring follow-on research.The Standard now addresses effects of elevated air movement, solar gain on the occupant, and draft at the ankles, each with several impacts on energy-efficient design and operation. It also addresses the most important source of discomfort in modern buildings, the large inter- and intra-personal variability in thermal comfort requirements, by classifying the occupants’ personal control and adaptive options in a form that can be used in building rating systems. In order to facilitate design, new computer tools extend the use of the standard toward direct use in designers’ workflow. The standard also includes provisions for monitoring and evaluating buildings in operation. This paper summarizes these developments and their underlying research, and attempts to look ahead

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here

Buildings-to-Grid Integration Framework

This paper puts forth a mathematical framework for Buildings-to-Grid (BtG) integration in smart cities. The framework explicitly couples power grid and building's control actions and operational decisions, and can be utilized by buildings and power grids operators to simultaneously optimize their performance. Simplified dynamics of building clusters and building-integrated power networks with algebraic equations are presented---both operating at different time-scales. A model predictive control (MPC)-based algorithm that formulates the BtG integration and accounts for the time-scale discrepancy is developed. The formulation captures dynamic and algebraic power flow constraints of power networks and is shown to be numerically advantageous. The paper analytically establishes that the BtG integration yields a reduced total system cost in comparison with decoupled designs where grid and building operators determine their controls separately. The developed framework is tested on standard power networks that include thousands of buildings modeled using industrial data. Case studies demonstrate building energy savings and significant frequency regulation, while these findings carry over in network simulations with nonlinear power flows and mismatch in building model parameters. Finally, simulations indicate that the performance does not significantly worsen when there is uncertainty in the forecasted weather and base load conditions.Comment: In Press, IEEE Transactions on Smart Gri

An online reinforcement learning approach for HVAC control

Heating, Ventilation and Air Conditioning (HVAC) optimization for energy consumption reduction is becoming ever more a topic of the utmost environmental and energetic concerns. The two most employed methodologies for optimizing HVAC systems are Model Predictive Control (MPC) and Reinforcement Learning (RL). This paper compares three different RL approaches to HVAC optimization: one based on a black-box system identification model trained on historical data, one based on a white-box model of a building and one online method based on an imitation learning pretraining phase on historical data. The three approaches are compared with a literature baseline and an EnergyPlus baseline. Results show that the overall best method in terms of energy consumption reduction (65% decrease) and thermal comfort increase (25% increase) is the approach based on the white-box model. However, the proposed methodology, based on online and imitation learning, demonstrates remarkable efficiency, achieving comparable improvements in energy consumption after just a few months of online training, while maintaining thermal comfort at around the same level as the baseline. These results prove a direct online RL approach, which avoid the use of costly simulations, can provide a reliable and inexpensive solution to the problem of HVAC optimization