Predicting energy consumption in Smart Buildings (SB), and scheduling it, is crucial for deploying Energy-efficient Management Systems. Most important, this constitutes a key aspect in the promising Smart Grids technology, whereby loads need to be predicted and scheduled in real-time to cope for the strongly coupled variance between energy demand and cost. Several approaches and models have been adopted for energy consumption prediction and scheduling. In this paper, we investigated available models and opted for machine learning. Namely, we use Artificial Neural Networks (ANN) along with Genetic Algorithms. We deployed our models in a real-world SB testbed. We used CompactRIO for ANN implementation. The proposed models are trained and validated using real-world data collected from a PV installation along with SB electrical appliances. Though our model exhibited a modest prediction accuracy, which is due to the small size of the data set, we strongly recommend our model as a blue-print for researchers willing to deploy real-world SB testbeds and investigate machine learning as a promising venue for energy consumption prediction and scheduling.

Water is becoming scarcer. The unmonitored control and the extensive use of fossil fuel in water-table pumping for irrigation exacerbate global warming and harm the environment. Along with the rapid population growth and the concomitant increase in the demand for food, optimal usage of water-table and energy is becoming a must and indispensable for sustainable agriculture. In this context, Smart Agriculture (SA) is emerging as a promising field that leverages ICT (Information and Communication Technology) to optimize resources’ usage while enhancing crops’ yields. In this paper, we present an integral SA solution that leverages cost-effectiveness . Commercial solutions are costly and thus become impossible to adopt by small and medium farmers. Our solution revolves around three main axes: 1. Smart Water Metering promotes optimal usage and conservation of water-table (a.k.a., groundwater) via real-time data collection and monitoring using a Cloud-based IoT (Internet of Things) system; 2. Renewable-Energy integration promotes energy-efficient agriculture by reducing reliance on fossil fuels in water-table pumping, and 3. Smart Irrigation to promote good crops quality and quantity without harming the soil and the water-table ecosystems. Our solution has been deployed and tested in a real-world Smart Farm testbed. The results have shown that the adoption of our SA system reduces the amount of water consumption (with a traditional irrigation system) up to 71.8%. Finally, our solution is open-source and can be easily adopted and adapted by other researchers to promote the setting of a dedicated Cloud-based platform for water-table usage, especially in arid and sub-Saharan countries.

This paper presents the architecture, performance characterization, and early scientific impact of Toubkal, Africa’s most powerful supercomputer (TOP500 #356, Green500 #178 as of June 2025). Designed to support high-performance computing research across disciplines, Toubkal integrates CPU, high-memory, and GPU-accelerated nodes connected via a high-bandwidth InfiniBand fabric and powered in part by renewable energy sources. We benchmark Toubkal’s compute, memory, network, and storage subsystems using standard and reproducible tools (HPL, STREAM, IOR, OMB, HPL-MxP), reporting robust performance and energy-efficiency metrics. The CPU partition achieves 63% of its theoretical peak, while GPU-accelerated HPL-MxP benchmarks on H100 nodes exceed 209 TFLOP/s per GPU. We also introduce a supervised learning model that predicts GPU frequency ranges minimizing the energy-delay product (EDP) across diverse workloads. The model achieves over 92% accuracy, with average gains of 13% in EDP, 13% in energy consumption, and a 1.1% reduction in kernel time across all kernels. The paper also compares Toubkal to several US DoE systems, analyzes early user growth, and documents real-world adoption across domains including AI, bioinformatics, and material science. These results offer both a technical reference for reproducibility and a performance baseline for emerging HPC centers worldwide. Beyond presenting a system deployment, this work contributes an early blueprint for sustainable, regional supercomputing infrastructure aligned with global standards.

Energy Management Systems (EMS) are indispensable for Smart Energy-Efficient Buildings (SEEB). This paper proposes a Wireless Sensor Network (WSN)-based EMS deployed and tested in a real-world smart building on a university campus. The at-scale implementation enabled the deployment of a WSN mesh topology to evaluate performance in terms of routing capabilities, data collection, and throughput. The proposed EMS uses the Context-Based Reasoning (CBR) Model to represent different types of buildings and offices. We implemented a new energy-efficient policy for electrical heaters control based on a Finite State Machine (FSM) leveraging on context-related events. This demonstrated significant effectiveness in minimizing the processing load, especially when adopting multithreading in data acquisition and control. To optimize sensors’ battery lifetime, we deployed a new Energy Aware Context Recognition Algorithm (EACRA) that dynamically configures sensors to send data under specific conditions and at particular times to avoid redundant data transmissions. EACRA increases the sensors’ battery lifetime by optimizing the number of samples, used modules, and transmissions. Our proposed EMS design can be used as a model to retrofit other kinds of buildings, such as residential and industrial, and thus converting them to SEEBs.