Resilient Electricity Load Forecasting Network with Collective Intelligence Predictor for Smart Cities

Electronics, Journal Year: 2024, Volume and Issue: 13(4), P. 718 - 718

Published: Feb. 9, 2024

Accurate electricity forecasting is essential for smart cities to maintain grid stability by allocating resources in advance, ensuring better integration with renewable energies, and lowering operation costs. However, most models that use machine learning cannot handle the missing values possess a single point of failure. With rapid technological advancement, are becoming lucrative targets cyberattacks induce packet loss or take down servers offline via distributed denial-of-service attacks, disrupting system inducing load data. This paper proposes collective intelligence predictor, which uses modular three-level networks decentralize strengthen against values. Compared existing models, it achieves coefficient determination score 0.98831 no using base model Level 0 network. As forecasted zone rise 90% single-model method longer effective, 0.89345 meta-model 1 network aggregate results from 0. Finally, as reach 100%, 0.81445 reconstructing forecast other zones 2

Language: Английский

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Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height

Energy, Journal Year: 2024, Volume and Issue: 294, P. 130887 - 130887

Published: March 2, 2024

Language: Английский

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A novel prediction model for integrated district energy system based on secondary decomposition and artificial rabbits optimization

Energy and Buildings, Journal Year: 2024, Volume and Issue: 310, P. 114106 - 114106

Published: March 28, 2024

Language: Английский

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Improving electricity demand forecasting accuracy: a novel grey-genetic programming approach using GMC(1,N) and residual sign estimation

Grey Systems Theory and Application, Journal Year: 2024, Volume and Issue: 14(4), P. 708 - 732

Published: May 29, 2024

Purpose This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance predictive performance of grey models by proposing a novel multivariate convolution model incorporating residual modification and genetic programming sign estimation. Design/methodology/approach research begins constructing demonstrates utilization prediction accuracy exploiting signs forecast residuals. Various statistical criteria are employed assess proposed model. validation process involves applying real datasets spanning from 2001 2019 for annual in Cameroon. Findings hybrid outperforms both non-grey consumption. model's is evaluated MAE, MSD, RMSE, R 2 , yielding values 0.014, 101.01, 10.05, 99% respectively. Results cases real-world scenarios demonstrate feasibility effectiveness combination offers significant improvement over competing models. Notably, dynamic adaptability enhances mimicking expert systems' knowledge decision-making, allowing identification subtle changes demand patterns. Originality/value introduces that incorporates application leveraging residuals represents unique approach. showcases superiority existing models, emphasizing its expert-like ability learn refine rules dynamically. potential extension other fields also highlighted, indicating versatility applicability beyond

Language: Английский

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Integrating Autoencoder and Decision Tree Models for Enhanced Energy Consumption Forecasting in Microgrids: A Meteorological Data-Driven Approach in Djibouti

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103033 - 103033

Published: Oct. 1, 2024

Language: Английский

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