Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Hu, Gang; Wang, Sa; Houssein, Essam H.

doi:10.1007/s42235-024-00549-9

J Bionic Eng

2024

DOI: 10.1007/s42235-024-00549-9

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Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Gang Hu,

Sa Wang,

Essam H. Houssein

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Cited by 2 publications

References 69 publications

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AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems

You,

Lu,

Qiu

et al. 2024

Biomimetics

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Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm’s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems.

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AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems

You,

Lu,

Qiu

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction

Huang,

Que,

Zeng

et al. 2024

Energies

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Accurate electricity consumption forecasting is essential for power scheduling. In short-term forecasting, electricity consumption data exhibit periodic patterns, as well as fluctuations associated with production events. Traditional forecasting methods typically focus on sequential features of the data, which may lead to an over-smoothing issue for the fluctuations. In practice, the fluctuations of electricity consumption associated with these events tend to follow recognizable patterns. By emphasizing the impact of these experiential electricity consumption fluctuations on the current prediction process, we can capture the volatility variations to alleviate the over-smoothing problem. To this end, we propose an encoding decomposition-based multi-scale graph neural network (CMNN). The CMNN starts by decomposing the electricity data into various components. For the high-order components that exhibit approximate periodic behavior, the CMNN designs a Multi-scale Bi-directional Long Short-Term Memory (MBLSTM) network for fitting and prediction. For the low-order components that exhibit fluctuations, the CMNN transforms these components from one-dimensional time series into a two-dimensional low-order component graph to model the volatility of the low-order components, and proposes a Gaussian Graph Auto-Encoder to forecast the low-order components. Finally, the CMNN combines the predicted components to produce the final electricity consumption prediction. Experiments demonstrate that the CMNN enhances the accuracy of electricity consumption predictions.

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Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Cited by 2 publications

References 69 publications

AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems

AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems

Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction

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