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The interest in sustainability and energy efficiency is constantly increasing, and the noticeable effects of climate change and rising energy prices are fueling this development. The residential sector is one of the most energy-intensive sectors and plays an important role in shaping future energy consumption. In this context, modeling has been extensively employed to identify relative key drivers, and to evaluate the impact of different strategies to reduce energy consumption and emissions. This article presents a detailed literature review relative to modeling approaches and techniques in residential energy use, including case studies to assess and predict the energy consumption patterns of the sector. The purpose of this article is not only to review the research to date in this field, but to also identify the possible challenges and opportunities. Mobility, electrical devices, cooling and heating systems, and energy storage and energy production technologies will be the subject of the presented research. Furthermore, the energy upgrades of buildings, their energy classification, as well as the energy labels of the electric appliances will be discussed. Previous research provided valuable insights into the application of modeling techniques to address the complexities of residential energy consumption. This paper offers a thorough resource for researchers, stakeholders, and other parties interested in promoting sustainable energy practices. The information gathered can contribute to the development of effective strategies for reducing energy use, facilitating energy-efficient renovations, and helping to promote a greener and more sustainable future in the residential domain. [ABSTRACT FROM AUTHOR]

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers' energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings. [ABSTRACT FROM AUTHOR]

Public buildings are energy-intensive users, especially when energy management is lacking. More than ever, the use of energy efficiency strategies and renewable energy sources (RES) in buildings are a national priority for Morocco in order to improve energy self-sufficiency, replace fossil fuel use and lower energy bills and greenhouse gas emissions. Relating to the exemplarity of the Moroccan government in terms of energy efficiency and sustainable development, the study support that aim and presents results of a deep energy performance analysis of more than 20 university campuses across Morocco, which has concluded that around 80% of the energy consumed in the university campuses is designated for lightning and hot water for sanitary use. Later, this study examined the potential for energy saving and the environmental benefits of implementing actions to reduce energy demand from the grid, considering the use of on-site solar energy. Thereafter, the study aimed to analyze the impact of RES integration in public university campuses, namely the photovoltaic (ESM1) for electricity output and solar thermal system for hot water use (ESM2), to assess the techno-economic-environmental performance on building energy consumption reduction. Hence, the paper reported a detailed energetic-economic and environmental (3E) analysis simulation for campuses by integration of the two Energy Saving Measurements (ESM). The results showed that the integration of ESM1 system can reduce the annual energy demand by 22% and the energy bill by 34%, whereas the integration of ESM2 achieved 67% in energy saving. According to the analysis of the results, the integration of ESM1 is expected to save 6044 MWh of electrical energy annually on the 30222 MWh for all campuses and 2559 MWh for ESM2 which is equivalent to 284 m3/yr of diesel. With the reduced energy consumption, it is possible to cut down fossil fuels for electricity production and offset greenhouse gas emissions by 672 tons of carbon dioxide annually. Besides, the evaluation of results showed that the energy performance indicator was reduced from 530 kWh/bed/yr to 248 kWh/bed/yr, which represents 56% of energy saving. [ABSTRACT FROM AUTHOR]

This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

Energy efficiency is one of the most relevant issues that the scientific community, and society in general, must face in the next years. Furthermore, higher energy efficiencies will contribute to worldwide sustainability. Buildings are responsible for 40% of the overall consumed energy. Smart Grids and Smart Buildings are playing an essential role in the definition of the next generation of sustainable Smart Cities. The main goal is reducing the impact of energy consumption on the environment as much as possible. This paper focuses on information communication technologies (ICTs) and techniques, their key characteristics and contribution to obtain higher energy efficiencies in smart buildings. Given that electrical energy is the most used, the investigation mainly centres on this energy. This paper also pays attention to green energies and energy harvesting due to their contribution to energy efficiency by providing additional clean energy. The main contribution of this investigation is pointing out the most relevant existing and emerging ICT technologies and techniques which can be used to optimize the energy efficiency of Smart Buildings. The research puts special attention on available, novel and emerging sensors, communication technologies and standards, intelligence techniques and algorithms, green energies and energy harvesting. All of them enable high-performance intelligent systems to optimize energy consumption and occupants' comfort. Furthermore, it remarks on the most suitable technologies and techniques, their main features and their applications in Smart Buildings. [ABSTRACT FROM AUTHOR]

Highlights • Overview of US Federal Energy Management (FEMP) lighting efficiency program. • Estimated total benefits of the 2017 lighting update from $33 to 217 million USD. • Estimated energy savings 0.33–4.76 TWh for this update. • Total benefits methodology with monetized full fuel cycle greenhouse gas emissions. • Presents the FEMP lighting categorization structure. Abstract Lighting consumes a great deal of electrical energy in buildings, and the U.S. federal government is the largest single energy consumer in the United States. Therefore, the way that the U.S. federal government manages its lighting energy use is of great importance. One important way that energy use is managed is through Federal Energy Management Program (FEMP) energy efficiency requirements. However, there is not much literature on how these requirements are set and how to value the resulting savings. This paper documents the methodology used to develop these for the 2017 FEMP lighting update. A methodology for calculating the total benefits is presented that goes beyond site energy saved, and includes full fuel cycle energy savings, energy cost savings, emissions offset and monetized emissions costs. The results show total benefits of $33 million USD including 475 kilotons of carbon dioxide (CO 2) offset under the low compliance scenario, $104 million USD including 1494 kilotons of CO 2 offset under the medium compliance scenario, and $217 million USD including 3124 kilotons of CO 2 offset under the high compliance scenario. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k -Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users. [ABSTRACT FROM AUTHOR]

Ubiquitous modernization of building energy and management systems brings tendencies for an effective integration of renewables in buildings. This article shows how solar energy can be in charge of energy savings within a building by integration of solar systems with Trombe wall. Numerical calculations of Trombe wall thermal resistance and its performances in comparison with a common wall are considered. Additionally, energy-efficiency gaining by a combination of PV modules and Trombe wall, using simulations by SAM (System Advisor Model) software is discussed. PV module parameters and their impact on its electricity production are observed and analyzed. The PV module parameters, analyzed in the paper are: the tilt angle α of PV modules, incident angle β of direct sunrays on PV modules, normal beam irradiation N and PV cells operating temperature. With bigger incident angle β of direct sunlight on PV modules, the normal beam irradiation is smaller, which brings less electricity production. The impact of PV module operating temperature can be neglected in comparison with the impact of tilt angle on PV module electricity production. The optimal tilt angle of PV modules for generating maximum annual electricity is around latitude angle. It varies with normal beam irradiation to achieve the maximum monthly electricity production. [ABSTRACT FROM AUTHOR]

Building energy efficiency has grown strong in a context of soaring energy prices, especially in Europe. The use of energy-saving devices strongly influences its improvement, but in many cases, it is far from sufficient., especially if the energy comes from renewable sources with forced production. In the case of buildings, these are usually photovoltaic (PV) sources. For this reason, energy management systems (EMS) are becoming increasingly popular as they allow the increase in self-consumption and reduce the size of energy storage. This article presents analyses of historical energy consumption profiles in selected small- and medium-sized buildings powered by renewable energy sources. The implementation limitations of this type of systems, depending on the profile of the building, were identified and guidelines were presented to assess low-cost solutions dedicated to small buildings and considering the actual conditions of existing systems. Statistical analyzes were conducted for the energy demand profiles of 15 different buildings. The analyzes consisted of the preparation of box plots for each hour of working days and the calculation of the relative standard deviation (RSD) index for annual profiles of 60 min periods. The analyzes showed that the RSD index has low values for commercial buildings (e.g., hospital 7% and bank 15%) and very high values for residential buildings—even over 100%. On this basis, it can be concluded about the usefulness of energy profiles for demand forecasting. The novelty of the proposed method is to examine the possibility of using measurement data as data to forecast energy consumption based on statistical analysis, dedicated to low-cost EMS system solutions. [ABSTRACT FROM AUTHOR]

• An overall panorama of the school buildings in Brazil was presented. • Energy billed data and survey evidence were used to build a statistical stock model. • The Weibull distribution was the most adequate to represent the continuous variables. • kWh/student·year was more suitable than kWh/m2·year to model Brazilian school buildings stock. • Thermal performance is an issue to be greatly improved in schools in Brazil. Understanding the actual conditions in school buildings regarding energy usage and environmental satisfaction gives way to the development of consistent building stock modelling. In the Brazilian context, a deep-rooted inefficient structure of energy management implies a lack of mapping the actual conditions of the public schools. Therefore, this paper aimed to assess the actual energy performance of school buildings in Brazil. This paper innovates by integrating questionnaire responses and billed energy data to allow the construction of an evidence-based stock model through a statistical approach, which was never performed for the Brazilian building stock. Moreover, the association of energy use intensity (EUI) with the core aspects of building characteristics, occupancy, environmental satisfaction and necessary improvements mapped the relevance of such aspects in energy usage patterns. Additionally, a discussion of the EUI indicators was addressed. Results showed an unprecedented broad panorama of the school building stock in Brazil and its main features. In terms of statistical analysis, the Weibull distribution was appropriate for representing the continuous variables of the stock. The EUI as a function of the number of students was more suitable than the floor-plan area to achieve a reliable energy performance analysis. Several tests were performed to assess if the energy management, environmental satisfaction and maintenance practices impacted the EUI or not. Finally, the main conclusion was that the stock model obtained was simple and fit the energy management structure in the Brazilian scenario. [ABSTRACT FROM AUTHOR]

Buildings are becoming suitable for application of sophisticated energy management approaches to increase their energy efficiency and possibly turn them into active energy market participants. The paper proposes a modular coordination mechanism between building zones comfort control and building microgrid energy flows control based on model predictive control. The approach opens possibilities to modularly coordinate technologically heterogeneous building subsystems for economically-optimal operation under user comfort constraints. The imposed modularity is based on a simple interface for exchanging building consumption and microgrid energy price profiles. This is a key element for technology separation, replication and up-scaling towards the levels of smart grids and smart cities where buildings play active roles in energy management. The proposed coordination mechanism is presented in a comprehensive realistic case study of maintaining comfort in an office building with integrated microgrid. The approach stands out with significant performance improvements compared to various non-coordinated predictive control schemes and baseline controllers. Results give detailed information about yearly cost-effectiveness of the considered configurations, which are suitable for deployment as short- and long- term zero-energy building investments. [ABSTRACT FROM AUTHOR]

This paper presents a series of control problems in prioritizing building energy retrofit and maintenance plans through a review of recent studies. The building energy retrofits can be strategically performed on policy level, management level, system level and unit level. Based on existing research efforts, this study casts the optimal building maintenance planning problem into a general control system framework. Unlike traditional control applications, this study argues that the control system framework is also applicable to the building energy management level, which will significantly improve the sustainability of realized energy savings and cost-effectiveness of building energy retrofits. In a general control framework, a number of research problems in the control systems are formulated, namely 1) control system decay dynamics modeling; 2) control system inputs and model uncertainties; 3) control system outputs; 4) control system uncertainties and disturbances; 5) control system algorithms; and 6) grouping and modeling. The proposed control problems bring out the intrinsic relationship of reliability engineering, maintenance engineering and control engineering in the broad directions of energy efficiency and optimization. Investigations into the proposed control problems will contribute to further improvements in the building energy retrofit and maintenance plans than the currently prevailing engineering practice. [ABSTRACT FROM AUTHOR]

Non-residential buildings like healthcare ones contribute to energy consumption and cause a negative environmental impact. This is mainly due to the age of the buildings, their poor level of energy efficiency and the implementation of only a basic maintenance plan. Owing to the very limited budget available for public administrators, Energy Performance Contracting (EPC), that entails the involvement of an Energy Service Company (ESCo), can provide the entire or part of the capital needed for investments aimed at progressively increasing energy efficiency over their service life. In this paper, three acute hospitals and two community clinics built in Italy are analysed, in order to assess the economic feasibility of several energy renovation actions that can be included within EPC contracts. To this purpose, the outcomes of energy audits carried out in 2014 about these buildings are reported, which involved analyses of consumption measured over the previous three years and assessment of use profiles for the development of models to break down the overall consumption and to estimate potential savings. Diverse improvement strategies were assessed, consisting in either isolated or combined renovation actions and tested on the five buildings, whose comparison was carried out in terms of energy saving, pollution reduction and economic feasibility. As a result, the best combined renovation actions per each hospital were recommended, that included – but were not limited to – the improvement of the control and regulation of existing sub-systems without replacement, partial replacement of other sub-systems and integration of renewables. Finally, each scenario was evaluated through the payback period and other economic performance indexes in order to assess the feasibility of the EPC framework when applied to this case study. [ABSTRACT FROM AUTHOR]

Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies. [ABSTRACT FROM AUTHOR]

This paper provides a comparative study of state-of-the-art means of predicting occupancy for smart heating control applications. We focus on approaches that predict the occupancy state of a home using occupancy schedules – that is, past records of the occupancy state. We ran our analysis on actual occupancy schedules covering several months for 45 homes. Our results show that state-of-the-art, schedule-based occupancy prediction algorithms achieve an overall prediction accuracy of over 80%. We also show that the performance of these algorithms is close to the theoretical upper bound expressed by the predictability of the input schedules. Building upon these results, we used ISO 13790-standard modelling techniques to analyse the energy savings that can be achieved by smart heating controllers that use occupancy predictors. Furthermore, we investigated the trade-off between achievable savings (typically 6–17% on average) and the risk of comfort loss for household residents. [ABSTRACT FROM AUTHOR]

Abstract: In an increasingly constrained context with severe requirements of quality, safety and environment respect, a building-integrated photovoltaic (BIPV) involves advanced local energy management rather than photovoltaic (PV) power permanent grid injection. This paper focus on building-integrated microgrid (BIMG) design and implementation applied to BIPV system with energy storage and smart grid communication. The goal is to propose power balancing strategy with smart grid interaction, aiming at reducing grid peak consumption, avoiding undesirable grid power injection, making full use of local PV production. The energy management is carried out considering grid time-of-use tariffs, grid access limits, storage capacity, load and PV power shedding if necessary. Based on experimental platform, the results show that the system maintains stability and confirm the relevance of the proposed local energy management giving perspectives on better integration of small PV plant in power grid. [Copyright &y& Elsevier]

This paper describes the design and the energy performance of a zero consumption building. The Passivhaus Standard is taken as a reference for the design of the building. The German Passivhaus standard is usually known for its low energy requirements that need low demand for heating and cooling. In addition, important issues to consider in the design stage of a building are analyzed in order to ensure efficient energy consumption. Once the final design of the building is defined, it is necessary to check that it is adequate. To achieve the objectives, the building energy simulation is used, the method that provides more realistic results. The simulation is performed with the software EnergyPlus (EP). Finally, to check the profitability of the building designed, a building whose envelopes and facilities solely satisfy the criteria established by the Spanish Building Technical Code (CTE) is analyzed and simulated. The results demonstrate that the passive building is profitable in few years. [ABSTRACT FROM AUTHOR]

Building information modeling can help in predicting the energy efficiency in future based on dynamic patterns obtained by visualization of data. The aim of this study was to investigate the effective parameters of energy consumption using BIM technology which can evaluate the buildings energy performance. First, three forms of general states in the building were modeled to evaluate the proposed designs in Autodesk Revit Software. Then, the main building form for energy modeling and analysis was selected. Autodesk Revit 2020 software was also used to obtain the results of climate data analysis and building energy consumption index. Finally, the most optimal mode was selected by examining different energy consumption modes. The results showed that the use of building information modeling technology in adjusting the parameters affecting energy consumption can save energy cost up to 58.23% in block D. Energy cost savings for block C and the western lobby were obtained as 51.03% and 43.05%, respectively. Based on energy use intensity, energy cost savings for blocks C, D, and the western lobby were estimated as 16.67%, 16.30%, and 11%, respectively. The results of parametric studies on alternative schemes of energy use intensity optimization showed that 16.30% savings could be achieved by the base building model in a 30-year time horizon. Therefore, it was concluded that optimization of energy consumption would reduce the environmental pollutants emission and contribute to preservation and sustainability of the environment. [ABSTRACT FROM AUTHOR]

rmation and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants' lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research. [ABSTRACT FROM AUTHOR]

Nowadays, buildings are increasingly expected to meet higher and more complex performance requirements. Among these requirements, energy efficiency is recognized as an international goal to promote energy sustainability of the planet. Different approaches have been adopted to address this goal, the most recent relating consumption patterns with human occupancy. In this work, we analyze what are the main parameters that should be considered to be included in any building energy management. The goal of this analysis is to help designers to select the most relevant parameters to control the energy consumption of buildings according to their context, selecting them as input data of the management system. Following this approach, we select three reference smart buildings with different contexts, and where our automation platform for energy monitoring is deployed. We carry out some experiments in these buildings to demonstrate the influence of the parameters identified as relevant in the energy consumption of the buildings. Then, in two of these buildings are applied different control strategies to save electrical energy. We describe the experiments performed and analyze the results. The first stages of this evaluation have already resulted in energy savings of about 23% in a real scenario. [ABSTRACT FROM AUTHOR]

The energy consumption of buildings is very important for both economic and environmental reasons. Newly built buildings are characterized by higher insulation and airtightness of the building envelope, and are additionally equipped with technologies that minimize energy consumption in order to meet legal requirements. In existing buildings, the modernization process should be properly planned, taking into account available technologies and implementation possibilities. Hotel buildings are characterized by a large variability of energy demand, both on a daily and a yearly basis. Monitoring systems, therefore, provide the necessary information needed for proper energy management in the building. This article presents an energy analysis of the Turówka hotel located in Wieliczka (southern Poland). The historical hotel facility is being modernized as part of the project to adapt the building to the requirements of a sustainable building. The modernization proposal includes a trigeneration system with a multifunctional reverse regenerator and control module using neural algorithms. The main purpose is to improve the energy efficiency of the building and adapt it to the requirements of low-energy buildings. The implementation of a monitoring system enables energy consumption to be reduced and improves the energy performance of the building, especially through using energy management systems and control modules. The proposed retrofit solution considers the high energy consumption, structure of the energy demand, and limits of retrofit intervention on façades. [ABSTRACT FROM AUTHOR]

Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces' heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space's occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building's living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron's (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building's living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed. [ABSTRACT FROM AUTHOR]