• Energy Research

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

This paper presents a proposal for the development of a new intelligent solution for the optimization of hybrid energy systems. This solution is of great importance for installers of hybrid energy systems, as it helps them obtain the best configuration of the hybrid energy system (efficient and less expensive). In this solution, it is sufficient to enter the name of the location of the hybrid energy system that we want to install; after that, the solution will show the name of the best technology from which the optimal configuration of this system can be obtained. To accomplish this goal, the study relied on the ontology approach for two reasons, one of which is related to the nature of hybrid systems, because it is characterized by a large amount of information that requires good structuring, and the second reason is the interaction of hybrid energy systems with the external environment (climate, site characteristics). Afterward, to develop the knowledge base of the ontology, many steps were followed, the first of which is related to a detailed study of the existing one and the extraction of the basic elements, such as the concepts and the relations between them, followed by the development of the rules of intelligent reasoning, which is an interaction between the elements of the ontology through which all possible cases are treated. The “Protégé” software was used to edit these elements and perform the simulation process to show the results of the developed solution. Finally, the paper includes a case study, and the results show the importance of the developed solution, and it is open to future developments.

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

Algeria is characterized by extreme cold in winter and high heat and humidity in summer. This leads to an increase in the use of electrical appliances, which has a negative impact on electrical energy consumption and its high costs, especially with the high price of electricity in Algeria. In this context, artificial intelligence can help to regulate the daily consumption of electricity, by optimizing the exploitation of natural resources and alerting the individual to avoid energy wasting. This paper proposes a decision-making tool (IRRHEM) for managing electrical energy at smart home. The IRRHEM solution is based on three elements: the use of natural resources, the notification of the inhabitants in case of resources misuse or wasting behavior, and the aggregation of similar activities at same time. Additionally, based on the proposed intelligent reasoning rules, residents’ behavior and activities are represented by OWL (Ontology Web Language) and written and executed through SWRL (Semantic Web Rule Language). Finally, the (IRRHEM) solution is tested in a home located in Algiers city inhabited by a family of four persons. The IRRHEM performance evaluation results are very promising and show a 3.60% rate of energy saving.

AbstractThe energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Le secteur public fournit des données ouvertes pour créer de nouvelles opportunités, stimuler l'innovation et mettre en œuvre de nouvelles solutions qui profitent au monde universitaire et à la société. Cependant, les données ouvertes sont généralement disponibles en grande quantité et manquent souvent de qualité, de précision et d'exhaustivité. Il peut être difficile de trouver les bonnes données pour analyser une cible. Il existe de nombreux référentiels de données ouvertes riches, mais ils sont difficiles à comprendre et à utiliser car ces données ne peuvent être utilisées qu'avec un ensemble complexe d'options de recherche par mot clé, et même alors, Des données non pertinentes ou insuffisantes peuvent éventuellement être récupérées. Pour remédier à cette situation, la recherche sémantique basée sur l'ontologie s'est avérée être un moyen efficace d'améliorer la qualité des requêtes de contenu connexes dans ces référentiels. Dans cet article, nous proposons une nouvelle méthode de liaison sémantique et de stockage d'ensembles de données gouvernementaux ouverts des secteurs de l'agriculture, des terres et des précipitations de la Nouvelle-Zélande, basée sur l'utilisation de l'ontologie. L'ontologie générée peut construire des données intégrées, dans lesquelles une requête unifiée peut être appliquée pour extraire des informations plus riches et plus utiles. Pour valider notre modèle, nous avons montré comment ontologie de lien manuellement et automatiquement. La liaison manuelle nécessite des experts de domaine, et la liaison automatique réduit le surcoût de s'appuyer sur des experts de domaine pour lier manuellement des concepts. Les résultats de cette méthode sont prometteurs en termes d'amélioration de la qualité des données et de l'efficacité de la recherche. À l'avenir, le modèle proposé peut être intégré à d'autres ontologies de domaine. El sector público proporciona datos abiertos para crear nuevas oportunidades, estimular la innovación e implementar nuevas soluciones que beneficien a la academia y la sociedad. Sin embargo, los datos abiertos generalmente están disponibles en grandes cantidades y, a menudo, carecen de calidad, precisión e integridad. Puede ser difícil encontrar los datos correctos para analizar un objetivo. Hay muchos repositorios de datos abiertos ricos, pero son difíciles de entender y usar porque estos datos solo se pueden usar con un conjunto complejo de opciones de búsqueda de palabras clave, e incluso entonces, datos irrelevantes o insuficientes pueden eventualmente recuperarse. Para aliviar esta situación, se ha demostrado que la búsqueda semántica basada en ontologías es una forma efectiva de mejorar la calidad de las consultas de contenido relacionado en dichos repositorios. En este documento, proponemos un nuevo método de vinculación semántica y almacenamiento de conjuntos de datos gubernamentales abiertos de los sectores de agricultura, tierras y precipitaciones de Nueva Zelanda basados en el uso de la ontología. La ontología generada puede construir datos integrados, en los que se puede aplicar una consulta unificada para extraer información más rica y útil. Para validar nuestro modelo, mostramos cómo enlace ontológico manual y automáticamente. El enlace manual requiere expertos en el dominio, y el enlace automático reduce la sobrecarga de depender de expertos en el dominio para vincular conceptos manualmente. Los resultados de este método son prometedores en términos de mejorar la calidad de los datos y la eficiencia de la búsqueda. En el futuro, el modelo propuesto se puede integrar con otras ontologías de dominio. The public sector provides open data to create new opportunities, stimulate innovation, and implement new solutions that benefit academia and society.However, open data is usually available in large quantities and often lacks quality, accuracy, and completeness.It may be difficult to find the right data to analyze a target.There are many rich open data repositories, but they are difficult to understand and use because these data can only be used with a complex set of keyword search options, and even then, irrelevant or insufficient data may eventually be retrieved.To alleviate this situation, ontology-based semantic search has been proven to be an effective way to improve the quality of related content queries in such repositories.In this paper, we propose a new method of semantic linking and storing open government datasets of New Zealand's agriculture, land and rainfall sectors based on the use of ontology.The generated ontology can construct integrated data, in which a unified query can be applied to extract richer and more useful information.To validate our model, we showed how to link ontology manually and automatically.Manual linking requires domain experts, and automatic linking reduces the overhead of relying on domain experts to manually link concepts.The results of this method are promising in terms of improving data quality and search efficiency.In future, the proposed model can be integrated with other domain ontologies. يوفر القطاع العام البيانات المفتوحة لخلق فرص جديدة، وتحفيز الابتكار، وتنفيذ حلول جديدة تفيد الأوساط الأكاديمية والمجتمع. ومع ذلك، عادة ما تكون البيانات المفتوحة متاحة بكميات كبيرة وغالبًا ما تفتقر إلى الجودة والدقة والاكتمال. قد يكون من الصعب العثور على البيانات الصحيحة لتحليل الهدف. هناك العديد من مستودعات البيانات المفتوحة الغنية، ولكن من الصعب فهمها واستخدامها لأنه لا يمكن استخدام هذه البيانات إلا مع مجموعة معقدة من خيارات البحث عن الكلمات الرئيسية، وحتى ذلك الحين، قد يتم في نهاية المطاف استرداد بيانات غير ذات صلة أو غير كافية. للتخفيف من هذا الوضع، ثبت أن البحث الدلالي القائم على الأنطولوجيا هو وسيلة فعالة لتحسين جودة استعلامات المحتوى ذات الصلة في مثل هذه المستودعات. في هذه الورقة، نقترح طريقة جديدة لربط وتخزين مجموعات البيانات الحكومية المفتوحة لقطاعات الزراعة والأراضي والأمطار في نيوزيلندا بناءً على استخدام الأنطولوجيا. يمكن للأنطولوجيا المتولدة بناء بيانات متكاملة، حيث يمكن تطبيق استعلام موحد لاستخراج معلومات أكثر ثراءً وفائدة. للتحقق من صحة نموذجنا، أظهرنا كيفية ربط الأنطولوجيا يدويًا وتلقائيًا. يتطلب الربط اليدوي خبراء المجال، والربط التلقائي يقلل من النفقات العامة للاعتماد على خبراء المجال لربط المفاهيم يدويًا. نتائج هذه الطريقة واعدة من حيث تحسين جودة البيانات وكفاءة البحث. في المستقبل، يمكن دمج النموذج المقترح مع أنطولوجيات المجال الأخرى.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.