An intelligent model for efficient load forecasting and sustainable energy management in sustainable microgrids

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: July 30, 2024

Abstract Microgrids have emerged as a promising solution for enhancing energy sustainability and resilience in localized distribution systems. Efficient management accurate load forecasting are one of the critical aspects improving operation microgrids. Various approaches prediction using statistical models discussed literature. In this work, novel framework that incorporates machine learning (ML) techniques is presented an solar wind generation. The anticipated approach also emphasizes time series-based microgrids with precise estimation State Charge (SoC) battery. A unique feature proposed utilizes historical data employs series analysis coupled different ML to forecast demand commercial scenario. Long Short-Term Memory (LSTM) Linear Regression (LR) employed experimental study under three cases, such (i) generation, (ii) and, (iii) SoC results show Random Forest (RF) LSTM performs well respectively. On other hand, Artificial Neural Network (ANN) model exhibited superior accuracy terms estimation. Further, Graphical User Interface (GUI) developed evaluating efficacy framework.

Language: Английский

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COSCO2 : AI ‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers

Transactions on Emerging Telecommunications Technologies, Journal Year: 2022, Volume and Issue: 34(1)

Published: Oct. 3, 2022

Abstract Workload prediction is the necessary factor in cloud data center for maintaining elasticity and scalability of resources. However, accuracy workload very low, because redundancy, noise, low center. Therefore, this article, a tree hierarchical deep convolutional neural network (T‐CNN) optimized with sheep flock optimization algorithm based work load proposed sustainable centers. Initially, historical from preprocessed using kernel correlation method. The T‐CNN approach used dynamic environment. weight parameters model are by algorithm. COSCO2 method has accurately predicts upcoming reduces extravagant power consumption at evaluated utilizing two benchmark datasets: (i) NASA, (ii) Saskatchewan HTTP traces. simulation implemented java tool calculated. From simulation, attains 20.64%, 32.95%, 12.05%, 32.65%, 26.54% high accuracy, 27.4%, 26%, 23.7%, 34.7%, 36.5% lower energy validating NASA dataset, similarly 20.75%, 19.06%, 29.09%, 23.8%, 20.5% 20.84%, 18.03%, 28.64%, 30.72%, 33.74% traces dataset than existing approaches, like auto adaptive differential evolution BiPhase learning‐based network, error preventive score time series forecasting models, methods prediction, self‐directed

Language: Английский

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PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction

Symmetry, Journal Year: 2023, Volume and Issue: 15(3), P. 613 - 613

Published: Feb. 28, 2023

To meet the increasing demand for its services, a cloud system should make optimum use of available resources. Additionally, high and low oscillations in workload are another significant symmetrical issue that necessitates consideration. A suggested particle swarm optimization (PSO)-based ensemble meta-learning forecasting approach uses base models PSO-optimized weights their network inputs. The proposed model employs blended learning strategy to merge three recurrent neural networks (RNNs), followed by dense layer. CPU utilization GWA-T-12 PlanetLab traces is used assess method’s efficacy. In terms RMSE, compared LSTM, GRU, BiLSTM sub-models.

Language: Английский

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Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments

Sensors, Journal Year: 2023, Volume and Issue: 23(15), P. 6911 - 6911

Published: Aug. 3, 2023

The Federated Cloud Computing (FCC) paradigm provides scalability advantages to Service Providers (CSP) in preserving their Level Agreement (SLA) as opposed single Data Centers (DC). However, existing research has primarily focused on Virtual Machine (VM) placement, with less emphasis energy efficiency and SLA adherence. In this paper, we propose a novel solution, Workload Prediction Deep Q-Learning (FEDQWP). Our solution addresses the complex VM placement problem, efficiency, preservation, making it comprehensive beneficial for CSPs. By leveraging capabilities of deep learning, our FEDQWP model extracts underlying patterns optimizes resource allocation. Real-world workloads are extensively evaluated demonstrate efficacy approach compared solutions. results show that DQL outperforms other algorithms terms CPU utilization, migration time, finished tasks, consumption, violations. Specifically, QLearning achieves efficient utilization median value 29.02, completes migrations an average 0.31 units, finishes 699 consumes least 1.85 kWh, exhibits lowest number violations 0.03 proportionally. These quantitative highlight superiority proposed method optimizing performance FCC environments.

Language: Английский

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A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center

2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Journal Year: 2021, Volume and Issue: unknown

Published: Dec. 17, 2021

It is vital to precisely forecast the workload of Virtual Machines (VMs) achieve efficient cloud resources management and reduce power consumption. In this research study, a deep learning-based hybrid strategy for VM prediction proposed. To create an accurate prediction, suggested model integrated convolutional neural network (CNN) architecture long-short-term memory (LSTM) network. The CNN component used elicit complex distinctive attributes data, while LSTM models temporal information predict future workload. Experimental results on real-world dataset have shown that proposed CNN-LSTM effective when compared frequently models, approach enhances forecasting performance.

Language: Английский

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DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure

Electronics, Journal Year: 2024, Volume and Issue: 13(18), P. 3588 - 3588

Published: Sept. 10, 2024

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services meet the performance requirements of users. The workload prediction cloud plays a crucial role in proactive auto-scaling and dynamic management resources move toward fine-grained load balancing job scheduling due its ability estimate upcoming workloads. However, users’ diverse usage demands, changing characteristics workloads have become more complex, including not only short-term irregular fluctuation but also long-term variations. This prevents existing workload-prediction methods from fully capturing above characteristics, leading degradation accuracy. To deal with problems, this paper proposes framework based on dual-channel temporal convolutional network transformer (referred as DuCFF) perform prediction. Firstly, DuCFF introduces data preprocessing technology decouple different components implied by combine original form new model inputs. Then, parallel manner, adopts convolution (TCN) channel capture local fluctuations time series Finally, features extracted two channels further fused, is achieved. proposed DuCFF’s was verified various benchmark datasets (i.e., ClarkNet Google) compared nine competitors. Experimental results show that can achieve average improvements 65.2%, 70%, 64.37%, 15%, respectively, terms Mean Absolute Error (MAE), Root Square (RMSE), Percentage (MAPE) R-squared (R2) baseline CNN-LSTM.

Language: Английский

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Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives

Tsinghua Science & Technology, Journal Year: 2024, Volume and Issue: 30(1), P. 34 - 54

Published: Sept. 11, 2024

Language: Английский

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Predicting the number of customer transactions using stacked LSTM recurrent neural networks

Social Network Analysis and Mining, Journal Year: 2021, Volume and Issue: 11(1)

Published: Sept. 27, 2021

Language: Английский

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A feature extraction and time warping based neural expansion architecture for cloud resource usage forecasting

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(4), P. 4963 - 4982

Published: Jan. 10, 2024

Language: Английский

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Hammer: A General Blockchain Evaluation Framework

Published: July 23, 2024

Language: Английский

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