Assessment of construction cost reduction of nearly zero energy dwellings in a life cycle perspective

Concerning Nearly Zero Energy Buildings, it is important to guarantee energy efficiency, thermal comfort and indoor environmental quality, while keeping construction and operational costs low. In this framework, this paper explores the efficacy of applying different scenarios, for reducing construction costs of new nearly zero energy multi-family houses in a life cycle perspective. Conversely to the standard cost-optimal approach, a real Italian case study building was chosen. Alternative and unconventional combinations of solutions for envelope and technical systems were adopted. Calculations were performed in two Italian cities (Rome and Turin). Three types of analysis were developed thermal comfort, energy performance and financial calculation. Results of the thermal analysis show that the installation of active cooling to prevent summer overheating can be avoided by applying low-cost passive strategies. All the proposed low-cost scenarios (4 alternative scenarios in Rome and 5 in Turin)reached the highest grade of energy performance, with a reduction of the non-renewable primary energy consumption up to 46% compared to the base case in Rome and 18% in Turin. From the economic perspective, all the scenarios in the two climate zones allow both reductions in the construction costs, up to 26% in Rome and 15% in Turin, and a Net Present Value after 50 years up to 163 €/m2 in Rome and 158 €/m2 in Turin

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Assessing the saving potential of blind controller via multi-objective optimization

This paper presents the results of computational experiments where multi-objective algorithms were used to tune a controller for blind movements in a residential building and a room of the LESO (Solar Energy and Building Physics Laboratory) experimental building. The blind controller, which is based on fuzzy logic, was optimized not only in terms of energy consumption but also in terms of thermal comfort. The goal is to show saving potential for intelligent blind controller in a real world example rather than in tailored idealized test rooms. Therefore, a state of the art simulation program with a multi-objective evolutionary algorithm was combined. It was found that with elementary control systems, like schedules for the lighting in a building, almost 40% of the energy could be saved. With the help of more advanced controllers this can be further increased. Also discussed in this paper are the results and the feasibility of implementing such a controlle

Gaps and requirements for applying automatic architectural design to building renovation

The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency

Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship

The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively

Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response

Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence

The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of the effectiveness and efficiency of integrative systems for renewable energy production from solar energy necessary to raise the threshold of self-produced energy, meeting the nZEB (near zero energy buildings) requirements