Recent advances on data-driven services for smart energy systems optimization and pro-active management

Optimization and proactive management of energy systems are crucial for achieving sustainability, efficiency and resilience in future smart energy networks. Data-driven approaches offer promising solutions for tackling the complex and dynamic challenges of energy systems, such as uncertainty, variability, and heterogeneity. Meanwhile, recent advances in decreasing hardware costs and improving data accessibility have allowed for the collection of high-quality data, leading to the development of more accurate and robust data-driven models of different energy systems. In this study, a comprehensive overview of current and future trends in data-driven optimization for smart energy systems is presented. After introducing the motivation and the background of this research field, the potential applications and benefits of optimization in various domains is discussed, such as electric vehicles charge, district heating networks and energy districts. Subsequently this review focuses on different methods and techniques for data-driven optimization and proactive management, ranging from scientific models to machine learning algorithms. Finally, the novel European project, DigiBUILD, is introduced, where different case studies are tested in several pilots, including electric vehicle charging management for increasing renewable energy source consumption, district heating network operative costs optimization and building energy and comfort management

Improving building occupant comfort through a digital twin approach:A Bayesian network model and predictive maintenance method

This study introduces a Bayesian network model to evaluate the comfort levels of occupants of two non-residential Norwegian buildings based on data collected from satisfaction surveys and building performance parameters. A Digital Twin approach is proposed to integrate building information modeling (BIM) with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort. The study also uses 200000 points as historical data of various sensors to understand the previous building systems’ behavior. The study also presents new methods for using BIM as a visualization platform and for predictive maintenance to identify and address problems in the HVAC system. For predictive maintenance, nine machine learning algorithms were evaluated using metrics such as ROC, accuracy, F1-score, precision, and recall, where Extreme Gradient Boosting (XGB) was the best algorithm for prediction. XGB is on average 2.5% more accurate than Multi-Layer Perceptron (MLP), and up to 5% more accurate than the other models. Random Forest is around 96% faster than XGBoost while being relatively easier to implement. The paper introduces a novel method that utilizes several standards to determine the remaining useful life of HVAC, leading to a potential increase in its lifetime by at least 10% and resulting in significant cost savings. The result shows that the most important factors that affect occupant comfort are poor air quality, lack of natural light, and uncomfortable temperature. To address the challenge of applying these methods to a wide range of buildings, the study proposes a framework using ontology graphs to integrate data from different systems, including FM, CMMS, BMS, and BIM. This study’s results provide insight into the factors that influence occupant comfort, help to expedite identifying equipment malfunctions and point towards potential solutions, leading to more sustainable and energy-efficient buildings.publishedVersio

Älykkäät huonejärjestelmät perusparannetuissa koulurakennuksissa

Smart technologies in buildings can improve user satisfaction, energy efficiency and the performance of technical systems. Demand-based ventilation and heating solutions are used to achieve great indoor environment quality energy efficiently. The European Commission has introduced a Smart Readiness Indicator in the new Energy performance of buildings directive, which aims at proving the added value that smart technologies bring to the building owners, users and tenants. The objectives of the thesis are to evaluate how ICT-technology and services can be used in buildings through case examples and to integrate different systems to co-operate including building automation, HVAC and a mobile application. The measurements were conducted in seven rooms in Aalto University’s Undergraduate center. The main improvements were: the monitoring and controllability of the variable air volume ventilation- and water radiator heating-system through Aalto space – mobile app, occupancy measurements and the collection of user satisfaction feedback. The VAV-ventilation system in the case rooms worked as designed. The CO2 concentration varied with each room, but the temperatures were nearly identical and stable. Three different control strategies for the ventilation were tested, where the combination of both temperature and CO2 concentration proved to be the best solution. The ratio between exhaust and supply air flows varied from room to room, best being 100% and worst 60%. This difference could be seen in the results of the pressure differences over the building envelope. This measurement was used to assess the performance of the ventilation system. All rooms were underpressured and there was a clear difference between day and night time pressures difference over the building envelope. During the night, the air handling unit of the zone serving the case rooms was not operating. Still during the nights, some general exhaust fan operating causing the greater underpressure. Room occupancy was measured with image- and CO2 concentration-based methods. Image-based methods provided varying results. The Kinect sensor had problems in identifying people, but the AXIS-3045 worked well with 95% accuracy. CO2 concentration-based method was accurate to one person 66% of the time and 89% accurate in identifying if the room is occupied or not. The error is caused by the latency of change of the concentration in the rooms. Also, the CO2 generation rates by humans and the accuracy of the supply and exhaust air flows can cause errors. User satisfaction in the rooms was measured with a paper survey and through Aalto space – mobile app. The results indicate that people are quite satisfied with the rooms as through the paper survey 71% answered +/- 1 on the PMV scale and through Aalto Space 84% answered either four or five stars out of five. Nearly half rated the indoor temperature as slightly cool/cool or cold. The indoor temperature was considered to be acceptable by 69% and the air quality by 79% of the respondents.Rakennusten älykkäät teknologiat parantavat käyttäjätyytyväisyyttä, energiatehokkuutta sekä rakennusten elinikää. Tarpeenmukaisen ilmanvaihdon ja lämmityksen ratkaisuilla saavutetaan energiatehokkaasti korkeatasoinen sisäilmaston laatu. Euroopan komissio on julkaissut uuden Smart Readiness indikaattorin, jonka tarkoituksena on korostaa älykkäiden teknologioiden tuoma lisäarvo rakennusten omistajille, käyttäjille sekä asukkaille. Tämän työn tavoitteena on arvioida miten ICT-teknologiaa ja palveluita voidaan käyttää rakennuksissa esimerkkitapausten avulla sekä integroida eri taloteknisiä ja muita järjestelmiä, kuten rakennusautomaation ja LVI:n sekä mobiilisovelluksen yhteen. Mittaukset toteutettiin seitsemässä huoneessa Aalto-yliopiston Kandidaattikeskuksessa. Tärkeimmät parannukset olivat: muuttuvan ilmavirtasääteisen ilmanvaihdon sekä vesiradiaattorijärjestelmän seuranta sekä ohjaus Aalto Space-mobiilisovelluksella, huoneiden käyttöasteen mittaus sekä käyttäjätyytyväisyys palautteen kerääminen. Muuttuva ilmavirtasääteinen ilmanvaihto toimi huoneissa kuten se oli suunniteltu. Sisäilmaolosuhteet vaihtelivat huoneiden välillä hiilidioksidipitoisuuden osalta, mutta lämpötila oli lähes identtinen jokaisessa huoneessa. Huoneissa testattiin kolmea eri ilmanvaihdonohjausstrategiaa, joista lämpötilan ja hiilidioksidipitoisuuden yhteisohjaus osoittautui parhaaksi ratkaisuksi. Myös tulo- ja poistoilmavirtojen suhde vaihteli huoneissa. Muutamissa huoneissa ilmavirrat olivat noin 100 % tasapainossa ja joissakin huoneissa suhde oli jopa 60 %. Tämä ero näkyi esimerkiksi huoneiden paine-eroissa rakennuksen vaipan yli. Paine-ero mittauksia tehtiin arvioidakseen ilmanvaihtojärjestelmän toimivuutta. Kaikki huoneet olivat alipaineisia. Alipaine oli selvästi suurempi öisin kuin päivisin. Tämä muutos johtuu siitä, että huoneiden ilmanvaihtokone on öisin pois päältä, mutta rakennuksessa on muita poistoilmanvaihtokoneita päällä. Käyttäjätyytyväisyyttä mitattiin kuudella kysymyksellä paperisena sekä Aalto Space -mobiilisovelluksen avulla. Tulokset osoittavat, että ihmiset ovat melko tyytyväisiä huoneiden sisäilmastoon, sillä paperikyselyiden kautta 71 % vastasi +/- 1 PMV-asteikolla ja Aalto Spacen kautta 84 % vastasi joko neljä tai viisi tähteä viidestä. Lähes puolet vastaajista kertoi sisälämpötilan olevan hieman viileä, viileä tai kylmä. Hyväksyttävänä sisälämpötilaa piti 69 % ja ilmanlaatua 79 % vastaajista

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings

Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data, occupants’ feedback, a probabilistic model of occupants’ comfort, and HVAC faults detection and prediction that may affect occupants’ comfort. New methods for using BIM as a visualization platform, as well as a pre- dictive maintenance method to detect and anticipate problems in the HVAC system, are also presented. These methods will help decision-makers improve the occupants’ comfort conditions in buildings. However, due to the intricate interaction between numerous equipment and the absence of data integra- tion among FM systems, CMMS, BMS, and BIM data are integrated in this paper into a framework utilizing ontology graphs to generalize the Digital Twin framework so it can be applied to many buildings. The results of this study can aid decision-makers in the facility management sector by offering insight into the aspects that influence occupant comfort, speeding up the process of identifying equipment malfunc- tions, and pointing toward possible solutions.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildingspublishedVersionPaid open acces

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