A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Γιαμαρέλος, Νικόλαος; Papadimitrakis, Myron; Stogiannos, Marios; Zois, Elias N.; Livanos, Nikolaos-Antonios I.; Alexandridis, Alex

doi:10.3390/s23125436

Cited by 5 publications

(2 citation statements)

References 108 publications

(121 reference statements)

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“…It entails forecasting a continuous output value by considering one or more input variables. In recent years, with the rise of big data and advancements in machine learning techniques, regression analysis has become a critical tool in various fields, including energy, finance, healthcare, and marketing [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Phan,

Pranata,

Farragher

et al. 2024

Sensors

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Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.

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Section: Introductionmentioning

confidence: 99%

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Phan,

Pranata,

Farragher

et al. 2024

Sensors

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show abstract

“…Therefore, efficient and reliable power grid systems are essential for maintaining power stability and avoiding power system outages and supply user load demands without power interruptions [ 1 , 2 ]. A sufficient power utilization scheme with accurate short-term load forecasting (STLF) is necessary for application on power grid systems [ 3 , 4 , 5 ]. One percent of forecasting error can cause operation losses of 10 million or more [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features

Chan,

Yiu,

Kim

et al. 2024

Sensors

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Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

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Sparse dynamic graph learning for district heat load forecasting

Huang,

Zhao,

Wang

et al. 2024

Applied Energy

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A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

Cited by 5 publications

References 108 publications

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features

Sparse dynamic graph learning for district heat load forecasting

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