• Energy Research

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

The smart energy consumption of any household, maintaining the thermal comfort level of the occupant, is of great interest. Sensors and Internet-of-Things (IoT)-based intelligent hardware setups control the home appliances intelligently and ensure smart energy consumption, considering environment parameters. However, the effects of environment-driven consumer body dynamics on energy consumption, considering consumer comfort level, need to be addressed. Therefore, an Energy Management System (EMS) is modeled, designed, and analyzed with hybrid inputs, namely environmental perturbations, and consumer body biological shifts, such as blood flows in skin, fat, muscle, and core layers (affecting consumer comfort through blood-driven-sensations). In this regard, our work incorporates 69 Multi-Node (MN) Stolwijik’s consumer body interfaced with an indoor (room) electrical system capable of mutual interactions exchange from room environmental parameters and consumer body dynamics. The mutual energy transactions are controlled with classical PID and Adaptive Neuro-Fuzzy-Type II (NF-II) systems inside the room dimensions. Further, consumer comfort, room environment, and energy consumption relations with bidirectional control are demonstrated, analyzed, and tested in MATLAB/Simulink to reduce energy consumption and energy cost. Finally, six different cases are considered in simulation settings and for performance validation, one case is validated as real-time hardware experimentation.

The smart energy consumption of any household, maintaining the thermal comfort level of the occupant, is of great interest. Sensors and Internet-of-Things (IoT)-based intelligent hardware setups control the home appliances intelligently and ensure smart energy consumption, considering environment parameters. However, the effects of environment-driven consumer body dynamics on energy consumption, considering consumer comfort level, need to be addressed. Therefore, an Energy Management System (EMS) is modeled, designed, and analyzed with hybrid inputs, namely environmental perturbations, and consumer body biological shifts, such as blood flows in skin, fat, muscle, and core layers (affecting consumer comfort through blood-driven-sensations). In this regard, our work incorporates 69 Multi-Node (MN) Stolwijik’s consumer body interfaced with an indoor (room) electrical system capable of mutual interactions exchange from room environmental parameters and consumer body dynamics. The mutual energy transactions are controlled with classical PID and Adaptive Neuro-Fuzzy-Type II (NF-II) systems inside the room dimensions. Further, consumer comfort, room environment, and energy consumption relations with bidirectional control are demonstrated, analyzed, and tested in MATLAB/Simulink to reduce energy consumption and energy cost. Finally, six different cases are considered in simulation settings and for performance validation, one case is validated as real-time hardware experimentation.

Las redes de sensores inalámbricos subacuáticos habilitadas para Internet de las cosas (IoT-UWSN) son bastante útiles para monitorear diferentes tareas, que incluyen: desde el monitoreo de instrumentos hasta el registro del clima y desde el control de la contaminación hasta la predicción de desastres naturales. Sin embargo, hay algunos desafíos que afectan el rendimiento de una red, es decir, la aparición de huecos, el alto consumo de energía (EC) y la baja relación de entrega de paquetes (PDR). Por lo tanto, en este trabajo, se proponen dos protocolos de enrutamiento de eficiencia energética para maximizar el PDR al minimizar la relación de ocurrencia de huecos. También se realiza un análisis de escalabilidad de los protocolos de enrutamiento propuestos. Además, se calculan las regiones factibles para comprobar la optimalidad del protocolo propuesto en términos de CE. Además, los protocolos propuestos se comparan con los protocolos de enrutamiento de referencia en contrapartes. Los resultados de la simulación muestran claramente que los protocolos de enrutamiento propuestos lograron una PDR un 80-81% más alta que el enrutamiento geográfico y oportunista con el control de topología basado en el ajuste de profundidad para la recuperación de la comunicación (GEDAR) y el nodo vecino de ajuste de transmisión que se aproxima a compañeros distintos de eficiencia energética (TA-NADEEM). Además, la relación de ocurrencia de huecos se minimiza hasta un 30% aproximadamente. Les réseaux de capteurs sans fil sous-marins activés par l'Internet des objets (IoT-UWSN) sont très utiles pour surveiller différentes tâches, notamment : de la surveillance des instruments à l'enregistrement du climat et du contrôle de la pollution à la prédiction des catastrophes naturelles. Cependant, il existe certains défis qui affectent les performances d'un réseau, à savoir l'apparition de trous vides, une consommation d'énergie élevée (EC) et un faible taux de livraison de paquets (PDR). Par conséquent, dans ce travail, deux protocoles de routage écoénergétiques sont proposés pour maximiser le PDR en minimisant le rapport d'occurrence des trous vides. Une analyse d'évolutivité des protocoles de routage proposés est également effectuée. En outre, des régions réalisables sont calculées pour vérifier l'optimalité du protocole proposé en termes de CE. En outre, les protocoles proposés sont comparés aux protocoles de routage de référence dans leurs homologues. Les résultats de la simulation montrent clairement que les protocoles de routage proposés ont atteint un PDR supérieur de 80 à 81 % à celui du routage géographique et opportuniste avec un contrôle de topologie basé sur l'ajustement de la profondeur pour la récupération des communications (GEDAR) et l'ajustement de la transmission en approchant des nœuds voisins distincts (TA-NADEEM). De plus, le rapport d'occurrence des trous vides est minimisé jusqu'à 30% environ. Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are quite useful in monitoring different tasks including: from instrument monitoring to the climate recording and from pollution control to the prediction of natural disasters. However, there are some challenges, which affect the performance of a network, i.e., void hole occurrence, high Energy Consumption (EC) and low Packet Delivery Ratio (PDR). Therefore, in this work, two energy efficient routing protocols are proposed to maximize the PDR by minimizing the ratio of void hole occurrence. Scalability analysis of the proposed routing protocols is also performed. Additionally, feasible regions are computed to check the optimality of the proposed protocol in terms of EC. Furthermore, proposed protocols are compared with benchmark routing protocols in counterparts. Simulation results clearly show that proposed routing protocols achieved 80-81% higher PDR than GEographic and opportunistic routing with Depth Adjustment based topology control for communication Recovery (GEDAR) and Transmission Adjustment Neighbor-node Approaching Distinct Energy Efficient Mates (TA-NADEEM). Moreover, the ratio of void hole occurrence is minimized upto 30% approximately. تمكّن إنترنت الأشياء شبكات الاستشعار اللاسلكية تحت الماء (IoT - UWSNs) مفيدة للغاية في مراقبة المهام المختلفة بما في ذلك: من مراقبة الأجهزة إلى تسجيل المناخ ومن التحكم في التلوث إلى التنبؤ بالكوارث الطبيعية. ومع ذلك، هناك بعض التحديات، التي تؤثر على أداء الشبكة، أي حدوث ثقب الفراغ، وارتفاع استهلاك الطاقة (EC) وانخفاض نسبة تسليم الحزمة (PDR). لذلك، في هذا العمل، يُقترح بروتوكولان للتوجيه موفران للطاقة لتعظيم PDR عن طريق تقليل نسبة حدوث ثقب الفراغ. كما يتم إجراء تحليل قابلية التوسع لبروتوكولات التوجيه المقترحة. بالإضافة إلى ذلك، يتم حساب المناطق الممكنة للتحقق من أمثلية البروتوكول المقترح من حيث المفوضية الأوروبية. علاوة على ذلك، تتم مقارنة البروتوكولات المقترحة مع بروتوكولات التوجيه المعيارية في النظراء. تُظهر نتائج المحاكاة بوضوح أن بروتوكولات التوجيه المقترحة حققت PDR أعلى بنسبة 80-81 ٪ من التوجيه الجغرافي والانتهازي مع التحكم الطوبولوجي القائم على ضبط العمق لاسترداد الاتصالات (GEDAR) وتعديل الإرسال للعقدة المجاورة التي تقترب من أصحاب كفاءة الطاقة المتميزين (TA - NADEEM). علاوة على ذلك، يتم تقليل نسبة حدوث ثقب الفراغ إلى 30 ٪ تقريبًا.

Las redes de sensores inalámbricos subacuáticos habilitadas para Internet de las cosas (IoT-UWSN) son bastante útiles para monitorear diferentes tareas, que incluyen: desde el monitoreo de instrumentos hasta el registro del clima y desde el control de la contaminación hasta la predicción de desastres naturales. Sin embargo, hay algunos desafíos que afectan el rendimiento de una red, es decir, la aparición de huecos, el alto consumo de energía (EC) y la baja relación de entrega de paquetes (PDR). Por lo tanto, en este trabajo, se proponen dos protocolos de enrutamiento de eficiencia energética para maximizar el PDR al minimizar la relación de ocurrencia de huecos. También se realiza un análisis de escalabilidad de los protocolos de enrutamiento propuestos. Además, se calculan las regiones factibles para comprobar la optimalidad del protocolo propuesto en términos de CE. Además, los protocolos propuestos se comparan con los protocolos de enrutamiento de referencia en contrapartes. Los resultados de la simulación muestran claramente que los protocolos de enrutamiento propuestos lograron una PDR un 80-81% más alta que el enrutamiento geográfico y oportunista con el control de topología basado en el ajuste de profundidad para la recuperación de la comunicación (GEDAR) y el nodo vecino de ajuste de transmisión que se aproxima a compañeros distintos de eficiencia energética (TA-NADEEM). Además, la relación de ocurrencia de huecos se minimiza hasta un 30% aproximadamente. Les réseaux de capteurs sans fil sous-marins activés par l'Internet des objets (IoT-UWSN) sont très utiles pour surveiller différentes tâches, notamment : de la surveillance des instruments à l'enregistrement du climat et du contrôle de la pollution à la prédiction des catastrophes naturelles. Cependant, il existe certains défis qui affectent les performances d'un réseau, à savoir l'apparition de trous vides, une consommation d'énergie élevée (EC) et un faible taux de livraison de paquets (PDR). Par conséquent, dans ce travail, deux protocoles de routage écoénergétiques sont proposés pour maximiser le PDR en minimisant le rapport d'occurrence des trous vides. Une analyse d'évolutivité des protocoles de routage proposés est également effectuée. En outre, des régions réalisables sont calculées pour vérifier l'optimalité du protocole proposé en termes de CE. En outre, les protocoles proposés sont comparés aux protocoles de routage de référence dans leurs homologues. Les résultats de la simulation montrent clairement que les protocoles de routage proposés ont atteint un PDR supérieur de 80 à 81 % à celui du routage géographique et opportuniste avec un contrôle de topologie basé sur l'ajustement de la profondeur pour la récupération des communications (GEDAR) et l'ajustement de la transmission en approchant des nœuds voisins distincts (TA-NADEEM). De plus, le rapport d'occurrence des trous vides est minimisé jusqu'à 30% environ. Internet of Things enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are quite useful in monitoring different tasks including: from instrument monitoring to the climate recording and from pollution control to the prediction of natural disasters. However, there are some challenges, which affect the performance of a network, i.e., void hole occurrence, high Energy Consumption (EC) and low Packet Delivery Ratio (PDR). Therefore, in this work, two energy efficient routing protocols are proposed to maximize the PDR by minimizing the ratio of void hole occurrence. Scalability analysis of the proposed routing protocols is also performed. Additionally, feasible regions are computed to check the optimality of the proposed protocol in terms of EC. Furthermore, proposed protocols are compared with benchmark routing protocols in counterparts. Simulation results clearly show that proposed routing protocols achieved 80-81% higher PDR than GEographic and opportunistic routing with Depth Adjustment based topology control for communication Recovery (GEDAR) and Transmission Adjustment Neighbor-node Approaching Distinct Energy Efficient Mates (TA-NADEEM). Moreover, the ratio of void hole occurrence is minimized upto 30% approximately. تمكّن إنترنت الأشياء شبكات الاستشعار اللاسلكية تحت الماء (IoT - UWSNs) مفيدة للغاية في مراقبة المهام المختلفة بما في ذلك: من مراقبة الأجهزة إلى تسجيل المناخ ومن التحكم في التلوث إلى التنبؤ بالكوارث الطبيعية. ومع ذلك، هناك بعض التحديات، التي تؤثر على أداء الشبكة، أي حدوث ثقب الفراغ، وارتفاع استهلاك الطاقة (EC) وانخفاض نسبة تسليم الحزمة (PDR). لذلك، في هذا العمل، يُقترح بروتوكولان للتوجيه موفران للطاقة لتعظيم PDR عن طريق تقليل نسبة حدوث ثقب الفراغ. كما يتم إجراء تحليل قابلية التوسع لبروتوكولات التوجيه المقترحة. بالإضافة إلى ذلك، يتم حساب المناطق الممكنة للتحقق من أمثلية البروتوكول المقترح من حيث المفوضية الأوروبية. علاوة على ذلك، تتم مقارنة البروتوكولات المقترحة مع بروتوكولات التوجيه المعيارية في النظراء. تُظهر نتائج المحاكاة بوضوح أن بروتوكولات التوجيه المقترحة حققت PDR أعلى بنسبة 80-81 ٪ من التوجيه الجغرافي والانتهازي مع التحكم الطوبولوجي القائم على ضبط العمق لاسترداد الاتصالات (GEDAR) وتعديل الإرسال للعقدة المجاورة التي تقترب من أصحاب كفاءة الطاقة المتميزين (TA - NADEEM). علاوة على ذلك، يتم تقليل نسبة حدوث ثقب الفراغ إلى 30 ٪ تقريبًا.

Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end-to-end delay and maximum energy dissipation. The provisioning of the aforementioned factors has become a challenging task for the research community. In UWSNs, battery consumption is inevitable and has a direct impact on the performance of the network. Most of the time energy dissipates due to the creation of void holes and imbalanced network deployment. In this work, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems which, due to which lifespan of the network increases. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform the counterparts.