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Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark
Published 2021“…One thousand distribution transformers' real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.…”
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Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing
Published 2023“…This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. …”
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3
A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System
Published 2021“…The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. …”
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Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
Published 2021“…<p>Different aggregation levels of the electric grid's big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. …”
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Impact of urban morphology on urban microclimate and building energy loads
Published 2021“…With the help of the Urban Weather Generator (UWG) and a locally established weather station, this research explores the validity of UWG processed open weather data (i.e., World Weather Online and Open Weather Map datasets). …”
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Short-Term Load Forecasting in Active Distribution Networks Using Forgetting Factor Adaptive Extended Kalman Filter
Published 2023“…A few research studies focused on developing data filtering algorithm for the load forecasting process using approaches such as Kalman filter, which has good tracking capability in the presence of noise in the data collection process. …”
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Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model
Published 2021“…The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. …”
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Time-constrained concurrent data-offloading in the cloud and fog
Published 2025“…<p dir="ltr">This paper discusses the concurrent offloading of data in a multi-platform configuration consisting of cloud and fog. devices feed these platforms with data that need to be stored, processed, and/or shared. …”
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An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals
Published 2020“…<p dir="ltr">Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. …”
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Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers
Published 2022“…</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3224796" target="_blank">https://dx.doi.org/10.1109/access.2022.3224796</a></p>…”
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Prediction the performance of multistage moving bed biological process using artificial neural network (ANN)
Published 2020“…To cope with this difficult task and perform an effective and well-controlled BP operation, an artificial neural network (ANN) algorithm was developed to simulate, model, and control a three-stage (anaerobic/anoxic and MBBR) enhanced nutrient removal biological process (ENR-BP) challenging real wastewater. The effect of surface area loading rate (SALR), organic matters (OMs), nutrients (N & P), feed flow rate (Q<sub>feed</sub>), hydraulic retention time (HRT), and internal recycle flow (IRF) on the performance of the ENR-BP to fulfil rigorous discharge limitations were evaluated. …”
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14
Application of human RNase P normalization for the realistic estimation of SARS-CoV-2 viral load in wastewater: A perspective from Qatar wastewater surveillance
Published 2022“…<p>The apparent uncertainty associated with shedding patterns, environmental impacts, and sample processing strategies have greatly influenced the variability of SARS-CoV-2 concentrations in wastewater. …”
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15
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
Published 2022“…This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. …”
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Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems
Published 2021“…Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.…”
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Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning
Published 2023“…<h3>Abstract</h3><p dir="ltr">In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. …”
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Reinforcement Learning Based EV Charging Management Systems–A Review
Published 2021“…However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. …”
