Building Energy Systems Research Articles

Multi-Objective Optimization of Insulation Thickness with Respect to On-Site RES Generation in Residential Buildings

This paper investigates the optimization of insulation thickness with respect to the integration of renewable energy systems in residential buildings in order to improve energy efficiency, maximize the contribution of renewables and reduce life cycle costs. Using the DesignBuilder and EnergyPlus software, this study models a representative two-story residential building located in Athens, Greece. The building envelope features extruded polystyrene thermal insulation and windows with unplasticized polyvinyl chloride frames and low-e glazing. Six scenarios with hybrid renewable energy systems are analyzed, including air- and ground-source heat pumps, solar thermal systems and a biomass fired boiler, so as to assess energy consumption, economic feasibility and internal air temperature conditions. A Pareto-fronts-based optimization algorithm is applied to determine the optimal combination of insulation thicknesses for the walls, the roof and the floor, focusing on minimizing the life cycle cost and maximizing the percentage of renewable energy utilized. The results demonstrate that scenarios involving biomass boilers and solar thermal systems, both for heating and cooling, when combined with reasonable thermal protection, can effectively meet the recent European Union’s directive’s goal, with renewable energy systems contributing more than 50% of the total energy requirements, whilst maintaining acceptable internal air temperature conditions and having a life cycle cost lower than contemporary conventional buildings.

Rationalising data collection for supporting decision making in building energy systems using value of information analysis

Rationalising data collection for supporting decision making in building energy systems using value of information analysis

Simulation of PSDF (Photovoltaic, Storage, Direct Current and Flexibility) Energy System for Rural Buildings

The PSDF (photovoltaic, storage, direct current, and flexibility) energy system represents an innovative approach aimed at achieving carbon neutrality. This study focused on rural buildings and utilized Modelica to develop a dynamic simulation model of the PSDF system. The research introduced a framework for direct current distribution microgrid systems with flexible regulatory mechanisms, employing a virtual inertia control strategy to provide stable adjustments for flexible operations and support integration with local grids. Case simulation results indicated that the system equipped with a water tank saved 3.15 kWh compared to the system without a water tank, resulting in an energy savings rate of 22.14%. Compared to traditional photovoltaic systems, the PSDF system significantly enhanced energy management flexibility and system reliability through the integration of thermal storage and battery management. This research made significant contributions to the fields of renewable energy and building energy systems by offering a scalable and practical solution suitable for rural contexts.

Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator

In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications.

The Building Decarbonization in High-Density Cities: Challenges and Solutions

Abstract Decarbonization of buildings is an imperative and challenging task. Beyond the common challenges associated with building decarbonization, those in high-density urban areas also face technical challenges due to geographical conditions and resource endowments. As decarbonization practices deepen, it has been found that reliance on conventional methods is fraught with difficulties, primarily due to the high proportion of incremental costs involved. This review study explores methods not widely incorporated into existing building energy efficiency standards but which hold the potential for aiding decarbonization. It advocates for a synergistic strategy involving surrounding infrastructure such as power and other building energy systems, innovative low-carbon building materials, and greenery to facilitate this transition.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Cost-effective and optimal pathways to selecting building microgrid components – The resilient, reliable, and flexible energy system under changing climate conditions

Amidst the changing climate conditions, electricity retailers-led demand response programs and the emergency backup power for possible grid outages are often discussed in isolation, although several underlying linkages exist, which are important for how the existing building stock evolves with the energy transition. This study navigates through the linkages while investigating the levelized cost of electricity (LCOE)-based building microgrid components and undertakes a comparative analysis of energy optimisation models with and without emergency diesel generators. It also examines the building energy system’s resilience, reliability, and flexibility by using OpenStudio and HOMER Grid to develop energy simulation and optimisation models and other probabilistic models for a chosen building archetype in Sydney, Australia. This study finds that excluding the emergency diesel generator will require a larger battery storage system, increasing LCOE from A$0.17/kWh to A$0.20/kWh across climate scenarios. However, the larger battery storage system increases the probability of surviving outages (> 4 h) by around 10 % across climate scenarios. The LCOE increases up to 45 % for outages up to 8 h across climate scenarios and up to 85 % for outages up to 12 h. Additionally, for the building archetype, the probability of grid purchase (below the current average) is 0.78 in 2030, which drops to 0.55 in 2050. The probability of grid sales (above the current average) also drops from 0.69 to 0.46. Thus, while the narratives around the building energy system’s flexibility overstate the electricity exchange between the building and the grid, this study finds that increasing the on-site production utilisation rate and larger battery throughput contributes to demand response application and flexibility.

Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

Net-Zero Scheduling of Multi-Energy Building Energy Systems: A Learning-Based Robust Optimization Approach With Statistical Guarantees

Heat pump and thermal energy storage: Influences of photovoltaic, the control strategy, and price assumptions on the optimal design

Combining heat pump, thermal energy storage, and photovoltaic is a common option to increase renewable energy usage in building energy systems. While research finds that optimal system design depends on the control, design guidelines neglect an influence of (1) photovoltaic, (2) the supervisory control, and (3) prices assumptions on the design of heat pump and thermal energy storage. We analyze how these influences may impact the optimal design. To analyze possible impacts, a pre-screening approach is used to identify relevant model input combinations with a potentially high influence on the optimal design. Applying a simulation-based design optimization with a detailed building energy system model, we optimize three cases: no photovoltaic, no supervisory control, and a state-of-the-art supervisory control. Results indicate that cost optimal design does not change with photovoltaic or the supervisory control, but heat pump size increases with higher electricity prices. For the storage, maximizing the size achieves high self-sufficiency degrees, but cost optimal design changes only for selected price assumptions. Overall, we find that neglecting photovoltaic and surplus control in the system design is a valid assumption. However, price assumptions should be included to allow simple but optimal design rules in current guidelines.

Evaluating conventional and renewable energy systems for green buildings: A case study on energy efficiency and cost optimization

This study explores two primary categories of power generation: conventional methods reliant on fossil fuels and renewable energy methods. Fossil fuel-based approaches, including natural gas and heavy fuel oil, contribute to environmental concerns due to their carbon dioxide (CO2) emissions, which are responsible for the greenhouse effect and global warming. Many nations, particularly in Europe, have implemented stringent measures to restrict CO2 emissions, imposing fines on industries that exceed emission limits. In response to these concerns, this study focuses on a green building project that aims to meet its power requirements through renewable energy sources. Various renewable energy resources will be evaluated, considering the merits and drawbacks of each technology, to identify the most suitable option for the building under investigation. Additionally, a comprehensive thermodynamic analysis will be conducted, including calculations of electrical needs such as cooling load, lighting load, and equipment load. A customized power calculator will also be developed for application in similar projects. The ultimate objective of this study is to achieve complete autonomy from the electric grid by utilizing renewable energy resources, transforming the building into a green building. The results indicate that at an outside temperature of 22 °C, the total heat gain decreases from 157.3 to 39 kW, reducing the total cost of electricity from 20,000 to 5100 AED/year. Conversely, at an outside temperature of 50 °C, the total heat gain increases from 157.3 to 196 kW, raising the total cost of electricity from 20,000 to 26,000 AED/year. These findings demonstrate the significant influence of outside temperature on the building's energy requirements and costs, underscoring the importance of considering temperature variations in energy efficiency and cost optimization strategies.

Quantifying the potential of load flexibility for building HVAC system using model predictive control strategy

Building as a Battery (BaB) is a concept aimed at utilizing building energy systems for load shifting purposes. However, there is a lack of study to accurately quantify building load flexibility and analyze how it is impacted by various factors. This study addresses this gap by first quantifying building load flexibility using battery parameters, specifically charging power, energy storage capacity, and round-trip efficiency. Subsequently, an analysis is conducted to examine the impacts of internal and external factors on building load flexibility, Specifically, we studied how ToU utility structure, load shifting duration, comfort range, ambient weather conditions, and building thermal properties influence charging power, energy storage capacity, and round-trip efficiency. Our simulation results revealed that an increase in the peak-to-valley ratio, load shifting duration, building thermal capacity, and cooling demand leads to higher charging energy and energy storage capacity, while an increase in building thermal resistance and widening of the comfort range result in their decrease. Regarding the round-trip efficiency, we found most parameters, except for building thermal capability, have a positive relation with the round-trip efficiency. Furthermore, sensitivity analysis showed that ToU utility structure and ambient weather conditions have the largest impacts on charging power, energy storage capacity, and round-trip efficiency. Additionally, our study indicates that leveraging buildings as virtual batteries for peak demand reduction is a feasible and cost-effective method compared with other energy storage techniques such as electric batteries. Overall, our findings offer valuable insights for parameterizing building load flexibility using battery parameters, analyzing factors that impact building load flexibility and informing grid operator to design property utility structures to meet their load shifting goals.

Life cycle carbon emission calculation model of energy system in public buildings: A case study in Shanghai

Energy consumption and carbon emissions (CE) in the building sector have grown in importance in relation to global climate change for China's sustainable development. Relatively few research have addressed the embodied CE, despite the fact that many have examined the operational CE from energy system operational use. In order to determine the CE of the energy production, transportation, construction, operation, and recycling stages, we therefore put up an integrated model. An inventory analysis of the energy system's life cycle CE, a scenario forecast of various combinations of building energy system equipment, and a case study of the building energy system of a typical office building in Shanghai's service industry. According to the results, the embodied CE emits 38.01 % of its energy, and the operational CE emits 61.99 %. However, we should not underestimate the significance of the impact of the embodied CE. Among all projection scenarios, the suggested CE share is highest (18.40 %) when the system combination of air-source heat pump units, LED lights, and ground-source heat pump units is implemented. In the end, based on the structural analysis of the embodied CE, low-carbon growth strategies and policy recommendations for Shanghai's service sector will be made.

A study on the enhancement of energy efficiency and indoor environment through the integration of solar photovoltaic modules in daylighting louver systems

The global energy demand is predicted to increase significantly, with a strong link between energy consumption and CO2 emissions. Lighting energy accounts for a substantial proportion of total energy consumption in buildings, with the potential to be reduced using daylighting louvers. Despite some drawbacks, integrating solar photovoltaic modules with louvers could enhance energy efficiency while addressing the increasing demand for renewable energy systems in new buildings. Therefore, this study explores the potential benefits of integrating solar photovoltaic (PV) modules into a daylighting louver system to simultaneously reduce lighting, cooling, and heating loads and generate solar power. The performance of the proposed louver system was experimentally compared to a conventional building-integrated photovoltaic (BIPV) system installed on a window. The daylighting louver system demonstrated significant improvements in indoor illumination and occupants’ experience compared to the BIPV system. Furthermore, the indoor air temperature in the space with the louver system increased by about 5 degrees compared to the BIPV system, indicating its contribution to heating load reduction. The power generation performance of the louver system reached up to 65 % of the BIPV system, with a total cumulative electricity generation of 64 %. The results suggest that the daylighting louver system can be expanded to enhance its efficiency and address the increasing demand for renewable energy system installations in new buildings. Future studies should investigate the potential of 2-axis or 3-axis louvers to further improve the system’s efficiency.

A district-level building electricity use profile simulation model based on probability distribution inferences

District-level building energy systems play a significant role in urban energy networks in the future. Understanding the key distributive features of district electricity use profiles is essential for the optimal planning and design of energy networks. Due to the diversity of building electricity use characteristics, the district-level electricity use profile exhibits a prominent “peak staggering effect.” Current physics-based and statistical models cannot fully represent realistic distributions and the uncertainties of district profiles. Thus, it is critical to quantitatively investigate the changing patterns and distributive features of electricity use profiles at various district levels. This paper proposes a novel approach for district building electricity use profile simulation. Probability distribution inference methods integrating Gaussian Mixture Model (GMM)/lognorm distribution fitting, singular value decomposition (SVD)-based feature transformation, and distribution addition theorems have been proposed to generate the feature parameters of electricity use profiles at various district scales, thus generating simulated district electricity use profiles. The performance of the proposed model was validated using engineering-informed metrics, including peak demands, load duration curves, and standard deviations of the load parameters. The results of the case study suggest that the average relative error of the 99 % peak demand is reduced from 17.60 % in the baseline model to 3.48 % in the proposed model, the average relative error of the duration of 2Qm reduced from 40.82 % in the baseline model to 0.99 % in the proposed model, and the average relative error of the standard deviation of load parameters was reduced from >100 % in the baseline model to <35 % in the proposed model. The results indicate a better quantification of district electricity use distributions and uncertainties, providing practical tools to support the capacity design and optimization of integrated district energy systems.

Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling

Machine learning is widely recognized as a promising data-driven modeling technique for the model-based control and optimization of building energy systems. However, the generalizability of data-driven models often faces significant challenges, as the available training data from building operations usually only covers a limited range of working conditions. Active learning can proactively test unseen and informative working conditions to enrich the training set by adding new data samples, leading to improved generalization performance of data-driven models. A novel distance and information density-based sample strategy is developed that accounts for the real-time status of building operation and outdoor environment. Based on Mahalanobis distance, this strategy determines the sampling value of an unlabeled sample (unseen working condition) by assessing its similarity to both the training samples and other unlabeled samples. As collecting sufficiently representative samples can be difficult, costly, and time-consuming, a distance-based sampling cost metric is proposed to compare the efficiency of different sampling methods, considering the detrimental effects of the actively sampling process on the normal operation of building energy systems. This paper presents a comprehensive and in-depth comparison of five active learning methods, including one incorporating the distance-based sampling strategy, by conducting data experiments on the data collected from the cooling towers of a real high-rise building. The results show that active learning can effectively identify informative data samples and improve the generalization performance of data-driven models. The research outcomes are valuable for enhancing AI-enabled data-driven modeling of building energy systems with substantial decreases in costs on data sampling.

Integrated Supervisory Control and Data Acquisition System for Optimized Energy Management: Leveraging Photovoltaic and Phase Change Material Thermal Storage

ABSTRACTReliable energy sources are crucial for both economic growth and quality of life. In developing countries, where expensive fuels are often the primary energy source, governments are exploring innovative solutions like small‐scale, IoT‐based projects to achieve energy independence in buildings. This research investigates the integration of renewable energy technologies, statistical modeling, cloud computing, and IoT to develop a self‐managing energy system for buildings. The system prioritizes renewable sources, specifically monocrystalline solar cells with 20% efficiency for photovoltaic (PV) energy and flat plate collectors with 90% efficiency and minimal energy loss for thermal energy. Thermal energy is stored in paraffin wax, chosen for its high storage efficiency and thermal properties. The system also utilizes an absorption chiller with a high coefficient of performance (COP) to provide cooling using solar thermal energy. The building's energy loads are categorized as A, B, C, and D, each utilizing both PV and thermal energy. A SCADA system oversees the operation, monitoring the on–off status of these loads. The system is designed for continuous operation, with simulations conducted using Anaconda Jupyter Notebook and Python. This model aims to offer a sustainable and efficient energy solution for buildings, meeting energy demands while optimizing energy use.

Online Operation Optimization for Hydrogen-Based Building Energy Systems Under Uncertainties

Online Operation Optimization for Hydrogen-Based Building Energy Systems Under Uncertainties

Economy and energy flexibility optimization of the photovoltaic heat pump system with thermal energy storage

Photovoltaic systems have been widely applied in building energy systems. However, with the sharp increase in demand of electricity in buildings during heating and cooling seasons, the uncertainty of photovoltaic generation further exacerbates the peak-valley difference in electricity use. To improve the economy and energy flexibility of buildings in hot summer and cold winter zones of China, a rule-based operation strategy was proposed for the photovoltaic heat pump with thermal energy storage system to optimize the size of the thermal energy storage system and system operation, with aims of minimizing the total annual cost of the system and maximizing the self-consumption rate of the photovoltaic generation. The effects of grid export power limits, grid import power limits, feed-in tariffs, and PV generation on the system operation optimization results were also investigated. The results indicated that by integrating the thermal energy storage system into the photovoltaic heat pump system, the self-consumption rate of the photovoltaic generation was reduced by 2.39 %, the total annual cost of the system was decreased by 6.61 %, and the payback period of the thermal energy storage system was 1.31 years. The optimum size of the thermal energy storage system and the self-consumption rate of the photovoltaic generation decreased with the increasing grid export power limits and grid import power limits. In contrast, higher grid export power limits, grid import power limits, feed-in tariffs, and PV generation all resulted in a lower total annual cost. This study provided a systematic design and operation optimization method for building energy systems.

Multi-objective coordinated optimization of low-carbon building energy systems based on high renewable energy penetration

In the context of China's targets to achieve a "carbon peak" by 2030 and "carbon neutrality" by 2060, the operation and maintenance of buildings with low-carbon and zero-energy consumption emerge as a critical technology for accomplishing the "dual carbon" goals in the building sector. As a novel energy supply system, low-carbon building energy systems can provide low-carbon, efficient, and economical energy supplies. Accordingly, this paper initially constructs a low-carbon building energy system based on a high penetration of renewable energy sources. Subsequently, considering the benefits of economic efficiency, carbon emissions, and renewable energy utilization, a multi-objective cooperative optimization model for low-carbon building energy systems with high renewable energy penetration is established. Finally, a multi-objective particle swarm optimization algorithm is applied to solve the model, and the system is studied and analyzed using a large domestic park as a case study. The results indicate that the low-carbon building energy system with high renewable energy integration, aiming for economic efficiency, carbon reduction, and increased renewable energy consumption, achieves supply energy costs of $ 19.2/m2 and carbon emissions of 49.1 kg/m2. These values represent reductions of $ 7.4/m2 and 60.9 kg/m2 compared to separate supply systems. Lastly, the study of the low-carbon building energy system provides theoretical references for renewable energy integration and energy transition in the building sector.

AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings

Despite the tightening of energy performance standards for buildings in various countries and the increased use of efficient and renewable energy technologies, it is clear that the sector needs to change more rapidly to meet the Net Zero Emissions (NZE) scenario by 2050. One of the problems that have been analyzed intensively in recent years is that buildings in operation use much more energy than they were designed to. This problem, known as the energy performance gap, is found in many countries and buildings and is often attributed to the poor management of building energy systems. The application of Artificial Intelligence (AI) to Building Energy Management Systems (BEMS) has untapped potential to address this problem and lead to more sustainable buildings. This paper reviews different AI-based models that have been proposed for different applications and different buildings with the intention to reduce energy consumption. It compares the performance of the different AI-based models evaluated in the reviewed papers by presenting the accuracy and error rates of model performance and identifies where the greatest potential for energy savings could be achieved, and to what extent. The review showed that offices have the greatest potential for energy savings (up to 37%) when they employ AI models for HVAC control and optimization. In residential and educational buildings, the lower intelligence of the existing BEMS results in smaller energy savings (up to 23% and 21%, respectively).