HELECAR-D: A dataset for urban electro mobility in Moroccan context.

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.

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation.

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.