A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks by Sakib Mahmud (15302404)

“…<p>The energy internet (EI) is evolving toward decentralized, data-rich, and time-critical operation, where legacy optimization often fails to meet complexity, scalability, and real-time constraints. …”

Effective dispatch strategies assortment according to the effect of the operation for an islanded hybrid microgrid by Sk.A. Shezan (21323048)

“…The proposed off-grid microgrid's CO₂emissions, total net present cost (NPC), and the Levelized cost of energy (LCOE) have all been optimized. In HOMER software, all the possible dispatch algorithms were analyzed, and the power system responses and reliability study were carried out using DIgSILENT PowerFactory. …”

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

“…The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. …”

Peak Loads Shaving in a Team of Cooperating Smart Buildings Powered Solar PV-Based Microgrids by Ahmed Ouammi (14151039)

“…The main objective is to formulate a constrained optimization problem embedded in a model predictive control (MPC) scheme to optimally control the operation of each microgrid to reduce/shave the peak load in case of occurrence, optimizing the power flows exchanges and energy storages, while ensuring a high quality of service to the EVs owners in each microgrid. …”

Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems by Khaled Dhibi (16891524)

Autonomous Demand-Side Management in the Future Smart Grid by Assaf, Tasneem Mohammad

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Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

“…Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. …”
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Spacecraft spin axis attitude determination by Emara-Shabaik, H.E.

“…The results show a tradeoff between estimation accuracy and computational requirements. One algorithm is about three times more accurate than the other and is therefore recommended even though it requires about 20% more in computer storage and operations, and about 50% more in central processing unit time…”
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Practical single node failure recovery using fractional repetition codes in data centers by Itani, May

“…Node failures in distributed storage systems are becoming a critical issue, and many erasure codes are designed to handle such failures. …”
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A Hardware Model of an Expandable RSA Cryptogrsphic System by Gutub, Adnan

“…Data security is an important aspect of information transmission and storage in an electronic form. Cryptographic systems are used to encrypt such information to guarantee its security. …”
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Hardware Model of an Expandable RSA Cryptographic System by Gutub, Adnan

“…Data security is an important aspect of information transmission and storage in an electronic form. Cryptographic systems are used to encrypt such information to guarantee its security. …”
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Fleet sizing of trucks for an inter-facility material handling system using closed queueing networks by Mohamed Amjath (17542512)

“…Within this context, the problem is modelled as a closed queueing network (CQN) combined with mixed-integer nonlinear programming (MINLP) to determine the optimal fleet size. This study proposes an analytical method based on sequential quadratic programming (SQP) methodology coupled with a mean value analysis (MVA) algorithm to solve this NP-Hard problem. …”

FoGMatch by Arisdakessian, Sarhad

“…Our solution consists of (1) two optimization problems, one for the IoT devices and one for the fog nodes, (2) preference functions for both the IoT and fog layers to help them rank each other on the basis of several criteria such latency and resource utilization, and (3) centralized and distributed intelligent scheduling algorithms that consider the preferences of both the fog and IoT layers to improve the performance of the overall IoT ecosystem. …”
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Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet by Vineet Dhanawat (22361395)

“…The model’s hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). …”

Hybrid deep learning based threat intelligence framework for Industrial IoT systems by Jahanzaib Malik (23718816)

“…The proposed approach was also compared against several contemporary deep learning-based architectures and existing benchmark algorithms. The results indicate that the proposed method achieves superior detection accuracy, with only a minimal compromise in speed efficiency. …”