School of Computer Science and Informatics

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https://hdl.handle.net/2086/63

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Browsing School of Computer Science and Informatics by Author "Abdelwahed, Sherif"

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    An Uncertainty Based Predictive Analysis of Smart Water Distribution System Using Bayesian LSTM Approach
    (IEEE, 2023-07-26) Zaman, Mostafa; Al Islam, Maher; Tantawy, Ashraf; Abdelwahed, Sherif
    A well-designed water distribution system is crucial for maintaining high service standards in any modern smart city. Moreover, as the population is sky-rocketing, the demand for energy and water is increasing more rapidly than a decade before. Therefore, ensuring a steady clean water supply with optimized energy and water consumption has become necessary. To accurately monitor water distribution systems, the accuracy of input data plays a vital role in determining how accurate the system’s status estimations are. There must be a way for system operators to know what is going on at any given time to make practical decisions about how reliable the data they are receiving is. The input data uncertainty can induce flow and pressure calculation inaccuracies, which can be fatal while planning for future demands and needs to be quantified.Knowing the degree of uncertainty in predicting the water distribution system’s capacity or load can help people better prepare for future capacity or load predictions. Accurate uncertainty calculations are critical to time series forecasting. Probabilistic formulae are widely employed with classical time series models to estimate uncertainty. But incorporating new data and fine-tuning these models is a challenging task. This research paper presents a Bayesian LSTM network that computes both time series prediction and uncertainty assessment at the same time. In this paper, a real-time data set from VCU’s OpenCity test bed is employed to evaluate the efficacy of the suggested strategy.
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    OpenCity: An Open Architecture Testbed for Smart Cities
    (IEEE, 2021-10-15) Zohrabi, Nasibeh; Martin, Patrick J.; Kuzlu, Murat; Linkous, Lauren; Eini, Roja; Morrissett, Adam; Zaman, Mostafa; Tantawy, Ashraf; Gueler, Oezguer; Al Islam, Maher; Puryear, Nathan; Kalkavan, Halil; Lundquist, Jonathan; Karincic, Erwin; Abdelwahed, Sherif
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    Optimizing Smart City Water Distribution Systems Using Deep Reinforcement Learning
    (IEEE, 2023-12-04) Zaman, Mostafa; Tantawy, Ashraf; Abdelwahed, Sherif
    Inefficient scheduling in water distribution systems can lead to energy waste, costly overflows, and a system that cannot keep up with demand. Simultaneous real-time management of system components such as pumps and valves to optimize operation in response to demand variations is a challenging task. Recent advances in deep reinforcement learning provides an opportunity to overcome the state explosion problem using function approximation to generalize from a limited interaction with the environment. In this work, we train a Long Short-Term Memory (LSTM) based Reinforcement Learning (RL) agent to optimize the energy usage of a smart water distribution system while maintaining a safe operating envelope. We compare the performance of the RL agent to two agents based on human experience in the domain; a baseline controller that is based on simple operational logic, and a fuzzy logic controller that captures imprecise human requirements. We show that the RL agent outperforms the other agents in terms of energy usage and operational safety, indicating its potential benefits for large-scale smart city systems. Future research work will focus on prioritized large-scale system scheduling to cope with smart city emergency situations.