Sustainable approaches for stormwater quality improvements with experimental geothermal paving systems

This article has been made available through the Brunel Open Access Publishing Fund.This research assesses the next generation of permeable pavement systems (PPS) incorporating ground source heat pumps (geothermal paving systems). Twelve experimental pilot-scaled pavement systems were assessed for its stormwater treatability in Edinburgh, UK. The relatively high variability of temperatures during the heating and cooling cycle of a ground source heat pump system embedded into the pavement structure did not allow the ecological risk of pathogenic microbial expansion and survival. Carbon dioxide monitoring indicated relatively high microbial activity on a geotextile layer and within the pavement structure. Anaerobic degradation processes were concentrated around the geotextile zone, where carbon dioxide concentrations reached up to 2000 ppm. The overall water treatment potential was high with up to 99% biochemical oxygen demand removal. The pervious pavement systems reduced the ecological risk of stormwater discharges and provided a low risk of pathogen growth

Evaluation of piezodiagnostics approach for leaks detection in a pipe loop

Pipe leaks detection has a great economic, environmental and safety impact. Although several methods have been developed to solve the leak detection problem, some drawbacks such as continuous monitoring and robustness should be addressed yet. Thus, this paper presents the main results of using a leaks detection and classification methodology, which takes advantage of piezodiagnostics principle. It consists of: i) transmitting/sensing guided waves along the pipe surface by means of piezoelectric device ii) representing statistically the cross-correlated piezoelectric measurements by using Principal Component Analysis iii) identifying leaks by using error indexes computed from a statistical baseline model and iv) verifying the performance of the methodology by using a Self Organizing Map as visualization tool and considering different leak scenario. In this sense, the methodology was experimentally evaluated in a carbon-steel pipe loop under different leaks scenarios, with several sizes and locations. In addition, the sensitivity of the methodology to temperature, humidity and pressure variations was experimentally validated. Therefore, the effectiveness of the methodology to detect and classify pipe leaks, under varying environmental and operational conditions, was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, and Self Organizing Maps, become as promising solution in the field of structural health monitoring and specifically to achieve robust solution for pipe leak detection.Peer ReviewedPostprint (author's final draft

Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway

The development of Smart Grid in Norway in specific and Europe/US in general will shortly lead to the availability of massive amount of fine-grained spatio-temporal consumption data from domestic households. This enables the application of data mining techniques for traditional problems in power system. Clustering customers into appropriate groups is extremely useful for operators or retailers to address each group differently through dedicated tariffs or customer-tailored services. Currently, the task is done based on demographic data collected through questionnaire, which is error-prone. In this paper, we used three different clustering techniques (together with their variants) to automatically segment electricity consumers based on their consumption patterns. We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes. We found that the combination of Self Organizing Map (SOM) and k-means algorithms produce the most insightful and useful grouping. We also discovered that grouping quality cannot be measured effectively by automatic indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure

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

Mapping Building Characteristics In Mitcham

The London Borough of Merton has begun the development of a district heat and power system. In order to financially model the feasibility of this scheme, energy consumption data for borough buildings must be known. The objective of this project was to investigate and apply methods for mapping the energy use of buildings within the borough of Merton. Difficulties encountered in data collection led to a revision of our project to include the drafting of sustainable information gathering mechanisms. With these in place the Merton Council will be able to base future plans on an accurate and thorough knowledge base

A Prototype Toolkit For Evaluating Indoor Environmental Quality In Commercial Buildings

Measurement of building environmental parameters is often complex, expensive, and not easily proceduralized in a manner that covers all commercial buildings. Evaluating building indoor environmental quality performance is therefore not standard practice. This project developed a prototype toolkit that addressed existing barriers to widespread indoor environmental quality performance evaluation. A toolkit with both hardware and software elements was designed for practitioners around the indoor environmental quality requirements of the American Society of Heating, Refrigeration and Air Conditioning Engineers / Chartered Institution of Building Services / United States Green Building Council Performance Measurement Protocols. This unique toolkit was built on a wireless mesh network with a web-based data collection, analysis, and reporting application. The toolkit provided a fast, robust deployment of sensors, real-time data analysis, Performance Measurement Protocol-based analysis methods and a scorecard and report generation tools. A web-enabled Geographic Information System-based metadata collection system also reduced field-study deployment time. The toolkit was evaluated through three case studies, which were discussed in this report

Post-Foreclosure Community Stabilization Strategies: Case Studies and Early Lessons 2008

In the midst of all the foreclosures sweeping the country, and the turmoil on Wall Street, nonprofit housing organizations are quietly going about the work of stabilizing communities hard hit by the crisis. Most have had frontline responsibility for counseling families threatened with foreclosure. With their assistance tens of thousands of families have restructured their budgets, negotiated with servicers to modify their loans, and saved their homes. Other families, too far along in the foreclosure process to stop it from happening, have received help transitioning to new housing arrangements.While the work with distressed homeowners must continue, nonprofits are feeling increased pressure to deal with the growing foreclosed housing stock. These units are causing incalculable harm to neighborhoods, and any hope of housing recovery must ensure that these units are swiftly put back into productive use or demolished. This collection of 14 case studies outlines strategies that nonprofit organizations across the country are using to begin the process of repairing damaged communities.The stakes are enormous. Vacant housing invites vandalism, and becomes a hub for gangs and crime. Virtually all case study subjects reported that, within weeks of housing becoming vacant, thieves break into the units and strip them of their valuable copper plumbing and wiring, heedless of any destruction they leave in their wake. In Phoenix a half-finished, abandoned subdivision was used as an informal "Home Depot" as other homeowners broke in and helped themselves to fixtures and appliances. In Cleveland, vandals remove not just the copper but the aluminum siding from vacant houses. In photos these houses have a desolate, post-disaster look, like the aftermath of a hurricane. When units get demolished the vacant lots soon sprout grass and trash, adding to the community's forlorn appearance.Vacant, deteriorated units place a downward pressure on housing values that puts nearby neighbors in a bind. In order to sell their units they will have to reduce the price, as no one will pay top dollar to live in a blighted neighborhood. Yet their ability to refinance into a more affordable mortgage may be compromised by the drop in property values; in some cases this leads to additional foreclosures and the downward cycle continues.Intervening in these troubled neighborhoods is challenging. In some markets housing prices are still falling, making it hard to determine the value of the units. Bank asset managers and servicers often lack detailed knowledge of the markets, or even of the units they have in their own inventory. This leads them to overvalue their properties and hold out for more than they are worth, delaying the process of acquiring and renovating them for resale to new homebuyers. Finally, the complex ownership structure of mortgages which were rolled into collateralized debt obligations and other investment vehicles makes it very difficult to establish who owns properties and who has authority to negotiate their sale.0

Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors include aspects such as weather dependence, user behaviour, socio-economic situation, type of the energy installations and typology of buildings. The high number of factors increases the complexity of analysis and leads to a lack of confidence in the results of the energy simulation analysis. This fact grows when we move one step up and perform global analysis of blocks of buildings. The aim of this study is to report a new methodology for the assessment of the energy performance of large groups of buildings when considering the real use of energy. We combine two clustering methods, Generative Topographic Mapping and k-means, to obtain reference dwellings that can be considered as representative of the different energy patterns and energy systems of the neighbourhood. Then, simulation of energy demand and indoor temperature against the monitored comfort conditions in a short period is performed to obtain end use load disaggregation. This methodology was applied in a district at Terrassa City (Spain), and six reference dwellings were selected. Results showed that the method was able to identify the main patterns and provide occupants with feasible recommendations so that they can make required decisions at neighbourhood level. Moreover, given that the proposed method is based on the comparison with similar buildings, it could motivate building occupants to implement community improvement actions, as well as to modify their behaviour

Symbiosis: An Interconnected Region for 2050

For the first time in history, the majority of the world's population is residing in its cities. Expended resources and climatic concerns are prompting a shift from traditional patterns of growth, predicated on the burning of fossil fuels, in favor of innovative, sustainable strategies. This thesis demonstrates the implications of this trend in the DC | Baltimore area, in the proposal of a closed-loop symbiotic-network city that will be linked both by alternative means of transit but more importantly by a lifeblood of inter-relational sustainable systems. The project's design develops at three scales; [xL] regional - through the establishment of an infrastructural and transit network between developable brownfield and greenfield sites in and around Baltimore and Washington DC, [L] district - in the development of one site into a mixed-use neighborhood, and [s] building - by the design of a civic edifice that serves as a pronounced model for the whole

Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

Building energy modeling plays an important role in analyzing the energy efficiency of the existing building stock, helping in enhancing it by testing possible retrofit scenarios. This work presents an urban scale and place-based approach that utilizes energy performance certificates to develop a statistical energy model. The objective is to describe the energy modeling methodology for evaluating the energy performance of residential buildings in Milan; in addition, a comprehensive reference dataset for input data from available open databases in Italy is provided a critical step in assessing energy consumption and production at territorial scale. The study employs open-source software QGIS 3.28.8 to model and calculate various energy-related variables for the prediction of space heating, domestic hot water consumptions, and potential solar production. By analyzing demand/supply profiles, the research aims to increase energy self-consumption and self-sufficiency in the urban context using solar technologies. The presented methodology is validated by comparing simulation results with measured data, achieving a Mean Absolute Percentage Error (MAPE) of 5.2%, which is acceptable, especially considering city-scale modeling. The analysis sheds light on key parameters affecting building energy consumption/production, such as type of user, volume, surface-to-volume ratio, construction period, systems’ efficiency, solar exposition and roof area. Additionally, this assessment attempts to evaluate the spatial distribution of energy-use and production within urban environments, contributing to the planning and realization of smart cities