Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Thermal performance of a naturally ventilated building using a combined algorithm of probabilistic occupant behaviour and deterministic heat and mass balance models

This study explores the role of occupant behaviour in relation to natural ventilation and its effects on summer thermal performance of naturally ventillated buildings. We develop a behavioural algorithm (the Yun algorithm) representing probablistic occupant behaviour and implement this within a dynamic energy simulation tool. A core of this algorithm is the use of Markov chain and Monte Carlo methods in order to integrate probablistic window use models into dynamic energy simulation procedures. The comparison between predicted and monitored window use patterns shows good agreement. Performance of the Yn algorithm is demonstrated for active, medium and passive window users and a range of office constructions. Results indicate, for example, that in some cases, the temperature of an office occupied by the active window user in summer is up to 2.6ºC lower than that for the passive window user. A comparison is made with results from an alernative bahavioural algorithm developed by Humphreys [H.B. Rijal, P. Tuohy, M.A. Humphreys, J.F. Nicol, A. Samual, J. Clarke, Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings, Energy and Buildings 39(7)(2007) 823-836.]. In general, the two algorithms lead to similar predictions, but the results suggest that the Yun algorithm better reflects the observed time of day effects on window use (i.e. the increased probability of action on arrival)

INTERACTIVE USER FRIENDLY SOFTWARE TO MODEL THE PRICE OF HIGH ENERGY SAVING LAMPS COMPARED TO INCANDESCENT LAMPS

The objective of this Final Year Project is to develop a probability and statistics software for an engineering application to model the price of lamps to its many desirable characteristics and then suggesting the most suitable lamp to be used. Users use this software-modeling tool to estimate the price of lamp and to forecast the billing cost for a specific lamp by using the power consumption and time ofusage. The proposed framework of the system consists of three inter related components; the database will provide input to the model, the modeling and user interface that provides a channel for the user to communicate with the system. Three stages have been identified in order to develop the system. They are variable identification, statistical model development and the development of the software. Severalvariables have been identified but it is observed that the main variables that is quantifiable and to have effect on price are wattage (W), brightness (lumens), diameter (mm), length (mm), average lifetime (hour) and Correlated Color Temperature CCT (K). The model that was identified is multiple regression analysis. This model is then incorporated in an interactive interface

Space after dark: Measuring the impact of public lighting at night on visibility, movement, and spatial configuration in urban parks

On 19 August 2016, Transport for London (TFL) launched their first Night Tube, which offers 24-hour service on Fridays and Saturdays. With more lines coming in autumn 2016, London follows the lead of other cities such as New York, Berlin, and Tokyo to be deemed a 24-hour city. Aside from debates concerning the energy waste, pollution, and security caused by the policy, one question is evident: How does the city 'work' at night? Humans navigate through space using vision which involves cue or landmark recognition, turn angle estimation, network comprehension, and route plotting strategies (Golledge, 1995). The situation changes at night when the configuration of space is altered by the presence of artificial light. This applies predominantly to outdoor spaces where lighting designers or urban planners classify the type of luminaires according to the street hierarchy: white light is used for the 'core' areas and main roads, yellow light for secondary roads, and reddish light for residential pathways (Meier, 2015, p.251). This study aims to explore whether or not there is a change of selected or most frequently used routes due to the impact of altered visual perception of space, and how the locus and quantity of the artificial illumination may change the perceived urban structure. It uses Dalton's (2001) research on cognition and movement, and the theory of natural movement (Hillier et al., 1993) as a base. Two parks in London were selected as the main case studies: Green Park and Clapham Common, along with a pilot project in The Meadows, Edinburgh. The parks were examined using a combination of street network analysis, detailed observations on people's movement and occupancy patterns, and survey on the existing lighting conditions. Correlations between movement, space, and lighting were analysed using 'multiple linear regression' method to discover a link between the fields of urban planning and lighting design. The results reveal that artificial illumination at night alters the perception of the spatial configuration. These results may contribute to the development of lighting master plans in cities. The research presented here produces parallel results with a recent study by Del-Negro (2015) that reveals how the lighting situation affects people's choice of routes through a series of experiments conducted in Lisbon and London. The correlation between Normalised Angular Choice and illuminance values suggests that these two factors are reliable predictors in an urban configuration at night, which allows us to use the illumination factor as a variable when applying Space Syntax analytical methods to predict nocturnal movement patterns

Application of users’ light-switch stochastic models to dynamic energy simulation

The design of an innovative building should include building overall energy flows estimation. They are principally related to main six influencing factors (IEA-ECB Annex 53): climate, building envelope and equipment, operation and maintenance, occupant behaviour and indoor environment conditions. Consequently, energy-related occupant behaviour should be taken into account by energy simulation software. Previous researches (Bourgeois et al. 2006, Buso 2012, Fabi 2012) already revealed the differences in terms of energy loads between considering occupants' behaviour as stochastic processes rather than deterministic inputs, due to the uncertain nature of human behaviour. In this paper, new stochastic models of users’ interaction with artificial lighting systems are developed and implemented in the energy simulation software IDA ICE. They were developed from field measurements in an office building in Prague. The aim is to evaluate the impact of a user's switching action over whole building energy consumption. Indeed, it is interesting not only to see the variance related to electric energy consumption, but the overall effect on a building's energy load

Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples

Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.This work was partly funded by the Department of Research and Universities of the Generalitat de Catalunya (grants 2017, SGR 646 and 2021 LLAV 00088), by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / ERDF (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project]) and by the Spanish Ministry of Science and Innovation / AEI/10.13039/501100011033 / European Union NextGeneration / PRTR (grantTED2021-131871B-I00 [DIGIFRUIT project]). We would also like to thank the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and the European Social Fund (ESF) for financing Juan Carlos Miranda’s pre-doctoral fellowship (2020 FI_B 00586). The work of Jordi Gené-Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU.info:eu-repo/semantics/publishedVersio

Monitoring System Analysis for Evaluating a Building’s Envelope Energy Performance through Estimation of Its Heat Loss Coefficient

The present article investigates the question of building energy monitoring systems used for data collection to estimate the Heat Loss Coefficient (HLC) with existing methods, in order to determine the Thermal Envelope Performance (TEP) of a building. The data requirements of HLC estimation methods are related to commonly used methods for fault detection, calibration, and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests to estimate the HLC undertaken since 1978, qualitative and quantitative analyses of the Monitoring and Controlling System (MCS) specifications have been carried out. The results show that no Fault Detection and Diagnosis (FDD) methods have been implemented in the reviewed literature. Furthermore, it was not possible to identify a trend of technology type used in sensors, hardware, software, and communication protocols, because a high percentage of the reviewed experimental tests do not specify the model, technical characteristics, or selection criteria of the implemented MCSs. Although most actual Building Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply HLC estimation methods.This work was supported by: Spanish Economy and Competitiveness Ministry and European Regional Development Fund through the IMMOEN project: "Implementation of automated calibration and multiobjective optimization techniques applied to Building Energy Model simulations by means of monitored buildings". Project reference: ENE2015-65999-C2-2-R (MINECO/FEDER); European Commission through the A2PBEER project "Affordable and Adaptable Public Buildings through Energy Efficient Retrofitting". Grant agreement No.: 609060; Laboratory for the Quality Control of Buildings (LCCE) of the Basque Government; University of the Basque Country (UPV/EHU). Framework agreement: Euro-regional Campus of Excellence within the context of their respective excellence projects, Euskampus and IdEx Bordeaux. Funder reference: PIFBUR 16/26

Machine Learning-based Real-Time Indoor Landmark Localization

Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones' indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones' locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning