Decision Support System for Evaluating Existing Apartment Buildings Based on Fuzzy Signatures

In historical district at European cities it is a major problem how to take decision on renovating or replacing existing buildings. This problem is imminent in Budapest (Hungary) in many traditional districts such as the Ferencváros district where we selected a compound area for further examination. By financial aid for the renovation of these buildings which awarded by Municipal Assembly of this district in question there is much uncertainty and confusion concerning how to decide whether or not and how to reconstruct a building where new private owners apply for support. In this paper we propose a formal evaluation method based on fuzzy signature rule bases (the formal being a special case of L-fuzzy object). Using the available expert knowledge we propose a fuzzy signature model including relevance weights and weighted aggregations for each node and parent node, respectively, so that as a result a single membership value may be calculated for each building in question. Linguistic labels for decision (such as worthless, average, highly valuable, etc.) are generated from the values thus obtained. Such linguistic calculations might be of help for the Municipal Assembly awarding financial support. A complete example wit 26 buildings is presented

Improving the Quality of Transport Management Services with Fuzzy Signatures

Nowadays the significance of road transport is gradually increasing. All transport companies are working in the same external environment where the speed of transport is defined by traffic rules. The main objective is to accelerate the speed of service and it is only dependent on the individual abilities of the managing members. These operational control units make decisions quickly (in a typically experiential and/or intuitive way). For this reason, support for these decisions is an important task. Our goal is to create a decision support model based on fuzzy signatures that can assist the work of operational management automatically. If the model sets parameters properly, the management of transport could be more economical and efficient

Building Ownership, Renovation Investments, and Energy Performance—A Study of Multi-Family Dwellings in Gothenburg

The European building stock was renewed at a rapid pace during the period 1950–1975. In many European countries, the building stock from this time needs to be renovated, and there are opportunities to introduce energy efficiency measures in the renovation process. information availability and increasingly available analysis tools make it possible to assess the impact of policy and regulation. This article describes methods developed for analyzing investments in renovation and energy performance based on building ownership and inhabitant socio-economic information developed for Swedish authorities, to be used for the Swedish national renovations strategy in 2019. This was done by analyzing measured energy usage and renovation investments made during the last 30 years, coupled with building specific official information of buildings and resident area characteristics, for multi-family dwellings in Gothenburg (N = 6319). The statistical analyses show that more costly renovations lead to decreasing energy usage for heating, but buildings that have been renovated during the last decades have a higher energy usage when accounting for current heating system, ownership, and resident socio-economic background. It is appropriate to include an affordability aspect in larger renovation projects since economically disadvantaged groups are over-represented in buildings with poorer energy performance

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Analysis on the Application of Machine-Learning Algorithms for District-Heating Networks' Characterization & Management

359 p.Esta tesis doctoral estudia la viabilidad de la aplicación de algoritmos de aprendizaje automático para la caracterización energética de los edificios en entornos de redes de calefacción urbana. En particular, la disertación se centrará en el análisis de las siguientes cuatro aplicaciones principales: (i)La identificación y eliminación de valores atípicos de demanda en los edificios; (ii) Reconocimiento de los principales patrones de demanda energética en edificios conectados a la red. (iii) Estudio de interpretabilidad/clasificación de dichos patrones energéticos. Análisis descriptivo de los patrones de la demanda. (iv) Predicción de la demanda de energía en resolución diaria y horaria.El interés de la tesis fue despertado por la situación energética actual en la Unión Europea, donde los edificios son responsables de más del 40% del consumo total de energía. Las redes de distrito modernas han sido identificadas como sistemas eficientes para el suministro de energía desde las plantas de producción hasta los consumidores finales/edificios debido a su economía de escala. Además, debido a la agrupación de edificios en una misma red, permitirán el desarrollo e implementación de algoritmos para la gestión de la energía en el sistema completo

Sustainable Development of Real Estate

Research, theoretical and practical tasks of sustainable real estate development process are revised in detail in this monograph; particular examples are presented as well. The concept of modern real estate development model and a developer is discussed, peculiarities of the development of built environment and real estate objects are analyzed, as well as assessment methods, models and management of real estate and investments in order to increase the object value. Theoretical and practical analyses, presented in the monograph, prove that intelligent and augmented reality technologies allow business managers to reach higher results in work quality, organize a creative team of developers, which shall present more qualitative products for the society. The edition presents knowledge on economic, legal, technological, technical, organizational, social, cultural, ethical, psychological and environmental, as well as its management aspects, which are important for the development of real estate: publicly admitted sustainable development principles, urban development and aesthetic values, territory planning, participation of society and heritage protection. It is admitted that economical crises are inevitable, and the provided methods shall help to decrease possible loss. References to the most modern world scientific literature sources are presented in the monograph. The monograph is prepared for the researchers, MSc and PhD students of construction economics and real estate development. The book may be useful for other researchers, MSc and PhD students of economics, management and other specialities, as well as business specialist of real estate business. The publication of monograph was funded by European Social Fund according to project No. VP1-2.2-ŠMM-07-K-02-060 Development and Implementation of Joint Master’s Study Programme “Sustainable Development of the Built Environment”

Zadeh's Centenary

This is the introductory paper in a special issue on fuzzy logic dedicated to the centenary of the birth of Lotfi A. Zadeh published by International Journal of Computers Communications & Control (IJCCC). In 1965, Lotfi A. Zadeh published in the journal „Information and Control” the article titled „Fuzzy sets”, which today reaches over 117 thousand citations. The total sum of citations for all his papers is above 253 thousand. Based on the notion of fuzzy sets, fuzzy logic and the concept of soft computing emerged, bringing extremely important implications to the field of Artificial Intelligence (AI). In 2017, I published, whith F.G. Filip and M.J. Manolescu, a 42-page long paper in the IJCCC about the life and masterwork of Lotfi A. Zadeh, from which I will use some information in this material [15]

Unsupervised monitoring of an elderly person\u27s activities of daily living using Kinect sensors and a power meter

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored. Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly. To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel. A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value. Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns. As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours. The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Energy Forensics Analysis

The energy consumed by a building can reveal information about the occupants and their activities inside the building. This could be utilized by industries and law enforcement agencies for commercial or legal purposes. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to foretell resident occupancy and type of appliance usage over desired time intervals. However, obtaining SM data in the United States is laborious and subjected to legal and procedural constraints. This research develops a user-driven simulation tool with realistic data options and assumptions of potential human behavior to determine energy usage patterns over time without any utility data. In this work, factors such as occupant number, the possibility of place being occupied, thermostat settings, building envelope, appliances used in households, appliance capacities, and the possibility of using each appliance, weather, and heating-cooling systems specifications are considered. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage possibility, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). This simulation generates energy usage patterns and electricity and natural gas costs over 30-minutes intervals for one year. The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as occupancy, appliance type, and time of the week. This work is intended to facilitate the analysis of building occupants\u27 activities by various stakeholders, subject to all legal provisions that apply. It is not intended for the general public to pursue these activities because legal ramifications might be involved