Annual Performance Of A Solar Assisted Heat Pump Using Ice Slurry As A Latent Storage Material

Solar assisted heat pump systems offer an attractive method of reducing the energy used for space heating and cooling, while efficiently using low temperature renewable energy from the sun to reduce the degradation of heat pump performance at low ambient temperatures. However, the majority of these systems use sensible storage to bridge the gap between thermal supply and demand, with the maximum storage capacity limited by physical constraints within the building. Latent storage has the potential to significantly reduce the required tank volumes in these types of systems. Previous work has demonstrated the benefit in heating mode of combining a solar heat pump system with ice based latent thermal storage, with this type of system achieving an up to 86% reduction in space heating energy use compared to a conventional system. The objective of this paper is to expand upon these findings and examine annual system performance in various Canadian climate regions through the evaluation of an innovative new operational mode providing space cooling to the building. The proposed system has distinct heating and cooling modes of operation. In heating mode, energy obtained from the solar collectors is stored in the ice tank. Thermal energy is then extracted from the ice tank using a heat pump, and delivered to a warm water tank acting as the distribution point for heating and DHW loops. An innovative new cooling mode is also presented, where the heat pump is used to build a cold storage reservoir for cooling purposes during the summer months. Excess thermal energy is then dissipated at night using radiative cooling (via solar collectors) or an air cooled condenser. Anticipated system benefits include increased energy storage densities, improved solar collector efficiencies, and potential utility cost savings by operating the heat pump during off-peak hours. To perform the analysis a computer model of the proposed system is developed using the TRNSYS energy simulation program, and integrated into high performance homes in three Canadian regions (Montreal, Toronto, Vancouver). Annual simulation results are presented and compared with typical base case designs in order to assess the viability and potential energy savings. A sensitivity analysis on several system variables is then presented in order to identify key design parameters for improved energy performance

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.

Multi-objective optimal design of a near net zero energy solar house

This paper presents a multi-objective redesign case study of an archetype solar house based on a near net zero energy (NZE) demonstration home located in Eastman, Quebec. Using optimization techniques, pathways are identified from the original design to both cost and energy optimal designs. An evolutionary algorithm is used to optimize trade-offs between passive solar gains and active solar generation, using two objective functions: net-energy consumption and life-cycle cost over a thirty-year life cycle. In addition, this paper explores different pathways to net zero energy based on economic incentives, such as feed-in tariffs for on-site electricity production from renewables. The main objective is to identify pathways to net zero energy that will facilitate the future systematic design of similar homes based on the concept of the archetype that combines passive solar design; energy-efficiency measures, including a geothermal heat pump; and a building-integrated photovoltaic system. Results from this paper can be utilized as follows: (1) systematic design improvements and applications of lessons learned from a proven NZE home design concept, (2) use of a methodology to understand pathways to cost and energy optimal building designs, and (3) to aid in policy development on economic incentives that can positively influence optimized home design

An information driven hybrid evolutionary algorithm for optimal design of a Net Zero Energy House

Building Performance Simulation (BPS) is a powerful tool to estimate and reduce building energy consumption at the design stage. However, the true potential of BPS remains unrealized if trial and error simulation methods are practiced to identify combinations of parameters to reduce energy use of design alternatives. Optimization algorithms coupled with BPS is a process-orientated tool which identifies optimal building configurations using conflicting performance indicators. However, the application of optimization approaches to building design is not common practice due to time and computation requirements. This paper proposes a hybrid evolutionary algorithm which uses information gained during previous simulations to expedite and improve algorithm convergence using targeted deterministic searches. This technique is applied to a net-zero energy home case study to optimize trade-offs in passive solar gains and active solar generation using a cost constraint

Parametric study of air curtain door aerodynamics performance based on experiments and numerical simulations

Air curtains have been widely used to reduce infiltration through door openings and save heating/cooling energy in different types of buildings. Previous studies have found that there exist three aerodynamics conditions: optimum condition (OC), inflow break-through (IB), and outflow break-through (OB) conditions, which are important for categorizing air curtain performance subject to such key parameters including supply speed and angle, and presence of a person during an actual operation. However, few studies have focused on the effects of these parameters on air curtain performance in terms of resisting infiltration and reducing exfiltration. This research presents a parametric study of air curtain performance based on reduced-scale experiments and full-scale numerical simulations. It was found that increasing air curtain supply angle improves air curtain performance when it is operated under the OC and IB conditions but creates excessive exfiltration under the OB condition. Increasing supply speed of air curtain generally improves the air curtain performance whereas this improvement deteriorates with the increase of supply angle under the OB condition. The presence of person, either directly under or below the air curtain, almost has no effect on the infiltration/exfiltration during the OC condition. Moreover, the person in the doorway can block airflow from both directions, contributing to less infiltration under the IB condition and less exfiltration under the OB condition than without the person. This study provides valuable insights into air curtain aerodynamics performance under different operational conditions and key contributing parameters

Machine Learning for Benchmarking Models of Heating Energy Demand of Houses in Northern Canada

In most cases, the benchmarking models of energy use in houses are developed based on current and past data, and they continue to be used without any update. This paper proposes the method of retraining of benchmarking models by applying machine learning techniques when new measurements are made available. The method uses as a case study the measurements of heating energy demand from two semi-detached houses of Northern Canada. The results of the prediction of heating energy demand using static or augmented window techniques are compared with measurements. The daily energy signature is used as a benchmarking model due to its simplicity and performance. However, the proposed retraining method can be applied to any form of benchmarking model. The method should be applied in all possible situations, and be an integral part of intelligent building automation and control systems (BACS) for the ongoing commissioning for building energy-related applications

Development and Testing of a Virtual Flow Meter Tool to Monitor the Performance of Cooling Plants

AbstractThis paper introduces a graphical user interface (GUI) for a virtual flow meter (VFM) Tool that estimates the chilled and condenser water mass flow rates through the evaporator and condenser, respectively, of a chiller. The quasi steady-state thermodynamic mathematical model uses six different scenarios of available sensors, which could be available in a building automation system (BAS) to facilitate the use of the model for a large portion of the building stock. The developed model can be used for reciprocating and centrifugal chillers. A case study from an institutional building is used to demonstrate the use of the VFM Tool during the summer months of 2014. The VFM Tool was able to estimate the chilled and condenser water mass flow rates with a coefficient of variance of the root mean squared error (CV(RMSE)) ranging from 4.9% to 13.4% and the normal mean bias error (NMBE) ranging from -12.2% to -1.4%, depending on the number of sensors available

Detection and Diagnosis of Dependent Faults That Trigger False Symptoms of Heating and Mechanical Ventilation Systems Using Combined Machine Learning and Rule-Based Techniques

Detection and diagnosis of the malfunction of the heating, ventilation, and air conditioning (HVAC) systems result in more energy efficient systems with a higher level of indoor comfort. The information from the system combined with the artificial intelligence methods contributes to powerful fault detection and diagnosis. The paper presents a novel method for the detection and diagnosis of multiple dependent faults in an air handling unit (AHU) of HVAC system of an institutional building during heating season. The proposed method guided the search for faults, by using the information and operation flow between sensors. Support vector regression (SVR) models, developed from building automation system (BAS) trend data, predicted air temperature of two target sensors, under normal operation conditions without known problems. The fault symptom was detected when the residual of measured and predicted values exceeded the threshold. The recurrent neural network (RNN) models predicted the normal operation values of regressor sensors, which were compared with measurements, as the first step for the identification of fault symptoms. Rule-based models were used for fault diagnosis of sensors or equipment. Results from a case study of an existing building showed the quality of proposed method for the detection and diagnosis of the multiple dependent faults

Residential Solar-Based Seasonal Thermal Storage Systems in Cold Climates: Building Envelope and Thermal Storage

The reduction of electricity use for heating and domestic hot water in cold climates can be achieved by: (1) reducing the heating loads through the improvement of the thermal performance of house envelopes, and (2) using solar energy through a residential solar-based thermal storage system. First, this paper presents the life cycle energy and cost analysis of a typical one-storey detached house, located in Montreal, Canada. Simulation of annual energy use is performed using the TRNSYS software. Second, several design alternatives with improved thermal resistance for walls, ceiling and windows, increased overall air tightness, and increased window-to-wall ratio of South facing windows are evaluated with respect to the life cycle energy use, life cycle emissions and life cycle cost. The solution that minimizes the energy demand is chosen as a reference house for the study of long-term thermal storage. Third, the computer simulation of a solar heating system with solar thermal collectors and long-term thermal storage capacity is presented. Finally, the life cycle cost and life cycle energy use of the solar combisystem are estimated for flat-plate solar collectors and evacuated tube solar collectors, respectively, for the economic and climatic conditions of this study