Deep configuration performance learning: A systematic survey and taxonomy
Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …
software system. However, given the increasing scale and complexity of modern software …
Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network
Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …
importance for energy management, infrastructure planning, and budgeting. In recent years …
Load forecasting under concept drift: Online ensemble learning with recurrent neural network and ARIMA
Rapid expansion of smart metering technologies has enabled large-scale collection of
electricity consumption data and created the foundation for sensor-based load forecasting …
electricity consumption data and created the foundation for sensor-based load forecasting …
Deterioration of electrical load forecasting models in a smart grid environment
Smart Grid (SG) is a digitally enabled power grid with an automatic capability to control
electricity and information between utility and consumer. SG data streams are heterogenous …
electricity and information between utility and consumer. SG data streams are heterogenous …
A power and thermal-aware virtual machine management framework based on machine learning
P **ao, Z Ni, D Liu, Z Hu - Cluster Computing, 2021 - Springer
Energy consumption in data centers grows rapidly in recent years. As a widely-applied
energy-efficient method, workload consolidation also has its own limitations that may bring …
energy-efficient method, workload consolidation also has its own limitations that may bring …
Accelerating the convergence of concept drift based on knowledge transfer
H Guo, Z Wu, Q Ren, W Wang - Pattern Recognition, 2025 - Elsevier
Abstract Concept drift detection and processing is an important issue in streaming data
mining. When concept drift occurs, online learning model often cannot quickly adapt to the …
mining. When concept drift occurs, online learning model often cannot quickly adapt to the …
High-Quality I/O Bandwidth Prediction with Minimal Data via Transfer Learning Workflow
Providing a high-quality performance prediction has the potential to enhance various
aspects of a cluster, such as devising scheduling and provisioning policies, guiding …
aspects of a cluster, such as devising scheduling and provisioning policies, guiding …
[HTML][HTML] Transfer-learning enabled adaptive framework for load forecasting under concept-drift challenges in smart-grids across different-generation-modalities
Abstract The Smart Grids (SGs) have significantly improved the load demand with the help of
different generation modalities (DGMs), that are supporting the energy demand. Equally, it …
different generation modalities (DGMs), that are supporting the energy demand. Equally, it …
[HTML][HTML] Mitigating concept drift challenges in evolving smart grids: An adaptive ensemble LSTM for enhanced load forecasting
This paper tackles the challenge of concept drift (CD), where data patterns evolve over time,
hindering the accuracy of traditional forecasting models in smart grids. The study proposes a …
hindering the accuracy of traditional forecasting models in smart grids. The study proposes a …
Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift
T Lazebnik - arxiv preprint arxiv:2412.09035, 2024 - arxiv.org
Data-driven models, in general, and machine learning (ML) models, in particular, have
gained popularity over recent years with an increased usage of such models across the …
gained popularity over recent years with an increased usage of such models across the …