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Integrating natural ventilation with Earth-to-Air Heat Exchangers (EAHE) is an innovative approach that effectively combines the benefits of both systems to enhance indoor environments while reducing energy consumption and operational costs. Many studies focus on reducing reliance on conventional heating, ventilation, and air conditioning systems by optimizing the use of EAHE and natural ventilation in buildings to improve occupant comfort. However, these studies require actual datasets for systems’ design and optimization. This work aims to provide such a dataset using a deployed EAHE with in-situ measurements. The dataset is available in its original form, allowing users to perform data preprocessing according to their specific needs. Additionally, we offer a processed version of the dataset, which can be used to further investigate the effectiveness of EAHE deployment in buildings and its integration with other passive and active systems, aiming to enhance energy efficiency while maintaining occupant comfort.

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach.

The integration of electric vehicles (EV) into the global car fleet has seen a major shift boosted by the new environmental regulations. The adoption of this low-carbon vehicle is hampered by several constraints in emerging countries and particularly in Morocco, e.g. constraints related to the infrastructure (land purchasing for charging stations, integration with existing power infrastructures, funding and optimized deployment) [1], and constraints related to the lack of standards and regulatory frameworks [2]. For this purpose, our objective is to share with the community a dataset about EV exploitation in the Moroccan context. This dataset [3] could be used to improve the energy management system characterized by a limited driving range and restrictive charging infrastructures. Subsequently, several driving cycles have been done in three main trajectories using data collection in the region of Rabat-Salé-Kénitra (RSK). The collected data contains mainly the date, time, Battery State of Charge (SoC), speed, vehicle's position, weather information, traffic conditions and road speed limits. The dataset collection is done using an onboard developed electronic card that collects the vehicle's internal and external data. Collected data are pre-processes and then stored in a Comma Separated Values (CSV) file. The collected dataset could be used in applications that are related to EV management and planning, such as speed prediction, speed control strategies, rerouting and EV charging scheduling, vehicle-to-grid and grid-to-vehicle, and energy demand forecasting.

Speed forecasting has numerous applications in intelligent transport systems’ design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles’ speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles’ characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting.

This article is an introductory work towards a larger research framework relative to Scientific Prediction. It is a mixed between science and philosophy of science, therefore we can talk about Experimental Philosophy of Science. As a first result, we introduce a new forecasting method based on image completion, named Forecasting Method by Image Inpainting (FM2I). In fact, time series forecasting is transformed into fully images- and signal-based processing procedures. After transforming a time series data into its corresponding image, the problem of data forecasting becomes essentially a problem of image inpainting, i.e., completing missing data in the image. Extensive experimental evaluation was conducted using the shortest series of the dataset proposed by the well-known M3-competition. Results show that FM2I, despite still being in its infancy, represents an efficient and robust tool for short-term time series forecasting. It has achieved prominent results in terms of accuracy and outperforms the best M3 methods. We have also investigated the effectiveness of the FM2I against the Smyl method, the winner of the M4 competition. Using the same category of shortest series, results show a close accuracy compared to the Smyl method. The FM2I is also able to generate ensemble data forecasts, which contain the best and more accurate forecast compared to existing and considered methods.

This paper investigates the numerical and experimental thermo-mechanical characterization of buildings’ structure for energy efficiency. A finite element model (FEM) of a small-scaled building is carried out using mesh refining tools at thermal bridges zones. The latter is revealed towards infra-red (IR) camera measurements. Galvanized steel is the main material composing the building’s walls in which heat transfer coefficients are determined based on measurements of external and internal averaged temperatures. Hence, the physical model includes thermal conductivity, heat convective and radiative phenomena. Results of computations, made with finite elements method, using the cast3m tool, show a good agreement compared to those obtained from experiments. Keywords: Energy efficiency in buildings, FEM, Infrared camera, Materials, Thermo-mechanical characterization

Morocco’s economy relies heavily on agriculture, which leads to an abundance in the availability of both vegetal and animal waste. Different treatment and disposal methods have been proposed to produce energy. Anaerobic digestion, being one of them, is being extensively used due to its lower environmental impact. In this paper, we are studying a wet, mesophilic, anaerobic co-digestion of animal and vegetable waste, using a pilot-scale anaerobic digester with liquid recirculation and immersed solid waste. In fact, this study could be divided into two main parts, the first one being a thermal study aiming to investigate the influence of the liquid recirculation’s flow rate and the substrate’s presence/absence on the heat transfer to the digester, and a second part presents a Computational Fluid Dynamics analysis (CFD) using FLUENT software tool, allowing a better understanding of the fluid’s behavior inside the digester. Simulations have been conducted and results show that a higher recirculation flow rate increases heat delivery to the effluent (from 0.18 to 0.66 kWh) but has a negative impact on pressure drop (from 429 to 1937.41 Pa). It was also observed that the liquid percolated more easily and through a larger portion of the waste at lower flow rates. Keywords: Anaerobic digestion, Biogas, Computational Fluid Dynamics analysis, Liquid recirculation, Thermohydraulic study

The integration of Combined Heat and Power (CHP) systems, especially fuel cell systems, into buildings have attracted a lot of interest these last years. It is due to their higher efficiency, cleanliness and their capability for producing both valuable thermal energy and electricity, from a single source of fuel, such as natural gas or hydrogen. Their high efficiency, which can reach 85%–90%, leads to a decrease in both greenhouse gas emissions and costs when compared to the conventional methods of generating heat and electricity separately. The aim of this work is towards efficient integration of a Proton Exchange Membrane (PEM) fuel cell into buildings in order to supply it with both electricity and heat. Experiments on a system composed of fuel cell, converter, and hydrogen tank have been carried out and compared to the simulations conducted using Matlab/Simulink where the system’s model has been developed. Simulation and experimental results are compared in order to show the accuracy of the models that have been developed and the efficiency of the fuel cell when operating as a CHP system to supply the building with both electricity and heat. Keywords: CHP system, Fuel cell, Hydrogen, Matlab/Simulink softwares

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality.

Building models and their connected subsystems are often simulated as standalone entities. However, in order to monitor a system′s reactions to changing parameters and to assess its energy efficiency, it must be exposed to the actual dynamic context of the building under study. Hence, frameworks assessing co-operative simulation of buildings and their subsystems should be used. In this study, the Building Control Virtual Test Bed (BCVTB) framework was used for co-simulation of a small-scale building (EEBLab) connected to an Earth-to-air heat exchanger (EAHE). The EnergyPlus tool was used to simulate the indoor air temperature variations within the EEBLab, and MATLAB was used to model the EAHE system and to calculate its performance based on various parameters. The HOLSYS internet of things platform was deployed to monitor and collect the experimental data from the sensors to validate the simulations. A favorable agreement between the experimental and simulation results was obtained, showing the contribution of the small-scale EAHE system in maintaining a comfortable indoor temperature range inside EEBLab. Moreover, it demonstrated the effectiveness and accuracy of the proposed approach for integrated building co-simulation and performance evaluation.

Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration, which usually do not match the occupants’ preferences. Therefore, occupancy detection is imperative for energy use management mainly in residential and industrial buildings. Most works related to data-driven-based occupancy detection have used batch learning techniques, which need to store first and then train the data. It is not appropriate for a non-stationary environment. Therefore, this work sheds more light on the use of non-stationary machine learning techniques. To this end, three machine learning algorithms for stream data processing are presented, tested, and evaluated in term of accuracy and resources performance (i.e., RAM, CPU), with the aim of predicting the number of occupants in smart buildings. A platform architecture that integrates IoT technologies with stream machine learning is implemented and deployed. The experimental results show the effectiveness of this approach and illustrate that the number of occupants can be predicted with an accuracy of more than 83% and without resource wasting (i.e., time of CPU use varied between 0.04s and 3.85 ⋅ 10−11 GB of RAM could be exploited per hour).

The availability of diverse and powerful sensors embedded in modern Smartphones/mobile devices has created exciting opportunities for developing context-aware applications. Although there is good capacity for collecting and classifying human activity data with such devices, data pre-processing and model building techniques that achieve this goal are required to operate while meeting hardware resource constraints, particularly for real-time applications. In this paper, we present a comparison study for HAR exploiting feature selection approaches to reduce the computation and training time needed for the discrimination of targeted activities while maintaining significant accuracy. We validated our approach on a publicly available dataset. Results show that Recursive Feature Elimination method combined with Radial Basis Function Support Vector Machine classifier offered the best tradeoff between training time/recognition performance.

In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.

In this study, a direct evaporative pad cooling system to be used for a poultry house is simulated under the climate conditions of six Moroccan cities. The objective is to optimize the parameters affecting the performance of the system for each city. The thermodynamic properties of humid air are extracted from the open source program CoolProp, and a computer program is used to perform a parametric study in order to find the impacts of the pad thickness and the frontal velocity on system's performance. Simulations have been conducted on cellulosic pad cooling and obtained results show that the system has the potential to lower the outdoor temperature during the hottest period with a coefficient of performance higher than 80, while the average rate of water consumption is higher than 3.3 kg per hour. Keywords: Direct evaporative pad cooling, Cellulosic, Simulated, Coefficient of performance, Optimization

In this paper, a finite-element-based model is being introduced and developed, using the Cast3m (CEA, Paris, France) simulation tool, to evaluate the thermo-mechanical behavior of a small-scale test bed. In fact, many studies on thermal behavior of cavities have been carried out in literature. However, none of them took into account the co-existence of all thermal phenomena (conduction, convection, internal/external radiation). The work presented in this paper presents a thermo-mechanical model, which aims to combine, in a holistic way, these phenomena. An experimental validation of the thermal model has been first carried out using an infrared camera and DS18B20 (Maxim Integrated Products, Dallas, TX, USA) numerical sensors. Results are reported and show the accuracy of the proposed model since both numerical and experimental values of heat transmittance fit together. The main objective is to evaluate heat losses through the walls, by means of heat transmittance calculation, and proposing new functional materials that will help in energy harvesting, as a perspective of this work. As for the mechanical study, it was meant to investigate the distribution of the mechanical stress towards the building envelope submitted to its own weight. Results showed that the stress is uniformly distributed on the lateral walls of the structure as well as on the floor.

The demand for electricity is increased due to the development of the industry, the electrification of transport, the rise of household demand, and the increase in demand for digitally connected devices and air conditioning systems. For that, solutions and actions should be developed for greater consumers of electricity. For instance, MG (Micro-grid) buildings are one of the main consumers of electricity, and if they are correctly constructed, controlled, and operated, a significant energy saving can be attained. As a solution, hybrid RES (renewable energy source) systems are proposed, offering the possibility for simple consumers to be producers of electricity. This hybrid system contains different renewable generators connected to energy storage systems, making it possible to locally produce a part of energy in order to minimize the consumption from the utility grid. This work gives a concise state-of-the-art overview of the main control approaches for energy management in MG systems. Principally, this study is carried out in order to define the suitable control approach for MGs for energy management in buildings. A classification of approaches is also given in order to shed more light on the need for predictive control for energy management in MGs.

The HVAC system represents the main auxiliary load in electric vehicles, but passengers’ thermal comfort expectations are always increasing. Hence, a compromise is needed between energy consumption and thermal comfort. The present paper proposes a real-time thermal comfort management strategy that adapts the thermal comfort according to the energy available for operating the HVAC system. The thermal comfort is evaluated thanks to the “Predicted Mean Vote”, representative of passenger’s thermal sensations. Based on traffic and weather predictions for a given trip, the algorithm first estimates the energy required for the traction and the energy available for thermal comfort. Then, it determines the best thermal comfort that can be provided in these energetic conditions and controls the HVAC system accordingly. The algorithm is tested for a wide variety of meteorological and traffic scenarios. Results show that the energy estimators have a good accuracy. The absolute relative error is about 1.7% for the first one (traction), and almost 4.1% for the second one (thermal comfort). The effectiveness of the proposed thermal comfort management strategy is assessed by comparing it to an off-line optimal control approach based on dynamic programming. Simulation results show that the proposed approach is near-optimal, with a slight increase of discomfort by only 3%.

The HVAC system represents the main auxiliary load in battery-powered electric vehicles (BEVs) and requires efficient control approaches that balance energy saving and thermal comfort. On the one hand, passengers always demand more comfort, but on the other hand the HVAC system consumption strongly impacts the vehicle’s driving range, which constitutes a major concern in BEVs. In this paper, a thermal comfort management approach that optimizes the thermal comfort while preserving the driving range during a trip is proposed. The electric vehicle is first modeled together with the HVAC and the passengers’ thermo-physiological behavior. Then, the thermal comfort management issue is formulated as an optimization problem solved by dynamic programing. Two representative test-cases of hot climates and traffic situations are simulated. In the first one, the energetic cost and ratio of improved comfort is quantified for different meteorological and traffic conditions. The second one highlights the traffic situation in which a trade-off between the driving speed and thermal comfort is important. A large number of weather and traffic situations are simulated and results show the efficiency of the proposed approach in minimizing energy consumption while maintaining a good comfort.

Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.

Quality of life, economic, knowledge and human capitals ‘development are the main challenges of the new wave of smart cities. Hybrid strategies of cost leadership and innovation need to be aligned mostly by highly deliberate university creative services. Physical, intellectual and social capitals are loosely coupled to better understanding of the urban fabric and norms of behavior. It requires the creation ofapplications enabling data collection and processing, web-based collaboration, and “real-time†mining of the collective intelligence of citizens. The Internet of Things (IoT) has been viewed as a promising technology with great potential for addressing many societal challenges, filling the gap in terms of citizen's sensitivity measurement. At the physical level of its ecosystem, buildings are responsible for about 40% of energy consumption in cities and more than 40% of greenhouse gas emissions. With recent products available today, energy consumption in buildings could be cut by up to 70 percent, but it requires an integrated and collective adaptive framework to show how buildings are operated, maintained and controlled with the support of IoT-based innovation and solutions. The number of new IoT protocols and applications has grown exponentially in recent years. However, IoT for smart cities needs accessible open data and open systems, so that industries and universities can develop new services and applications. The main aim is to develop energy efficient frameworks to improve energy efficiency by using innovative integrated IoT techniques. These techniques could integrate technologies from context-aware computing, context-dependent user expectation and profile and occupants' actions and behaviors. This paper tend to present in what extent a case of university-based smart city would invest in IoT as both strategy and process in order to enhance efficiency, innovative education and attractiveness for its current and future citizens.

Micro-Grid (MG) systems have been extensively studied and deployed to lower the power consumption while reducing the greenhouse gas emissions. Although, the challenge with Renewable Energy Sources (RES) is their uncertain and intermittent nature, things that led the researchers to think about integrating storage devices, especially batteries, into MG systems. The main aim is to store the excess of produced energy for further usage when not enough energy is available. Nevertheless, batteries modeling and characterization is mandatory to identify their parameters and study their performance within MG systems. Moreover, in order to continuously supply electricity to the building, it is required to figure out the optimum size of energy production systems and storage devices. This paper introduces a methodology for MG modeling and performance evaluation. Its main contribution is twofold, (i) battery’s parameters identification, and (ii) modeling and dimensioning method for both standalone and MG systems. An instrumentation platform, composed of recent sensing and actuating equipment, for MG energy management and battery characterization is developed. Simulation and experimental results show the effectiveness of the proposed methodology.

The degradation of photovoltaic (PV) modules remains a major concern on the control and the development of the photovoltaic field, particularly, in regions with difficult climatic conditions. The main degradation modes of the PV modules are corrosion, discoloration, glass breaks, and cracks of cells. However, corrosion and discoloration remain the predominant degradation modes that still require further investigations. In this paper, a model-based PV corrosion prognostic approach, based on an ensemble Kalman filter (EnKF), is introduced to identify the PV corrosion parameters and then estimate the remaining useful life (RUL). Simulations have been conducted using measured data set, and results are reported to show the efficiency of the proposed approach.