Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach

Yang, Dongchuan; Guo, Jiale; Li, Yanzhao; Sun, Shaolong; Wang, Shouyang

doi:10.1016/j.energy.2022.125609

Cited by 29 publications

(5 citation statements)

References 64 publications

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“…The performance of STLF models depends on various factors, such as the size and quality of the data, the forecasting horizon, and the complexity of the underlying relationships between the variables. Intelligent models outperform statistical models, and hybrid models outperform both statistical and intelligent models [90]. However, the models' relative performance can vary depending on the application and dataset.…”

Section: Performance Comparison Of Stlf Modelsmentioning

confidence: 99%

Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Akhtar,

Shahzad,

Zaheer

et al. 2023

Energies

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Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.

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Section: Performance Comparison Of Stlf Modelsmentioning

confidence: 99%

Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Akhtar,

Shahzad,

Zaheer

et al. 2023

Energies

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“…An LSTM was trained with the extracted components. Yang et al [27] proposed a decomposition approach to extract the time-series components of past load consumption. The decomposition approach captures useful past load consumption components to train DNNs.…”

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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“…Accurate load forecasting offers numerous advantages for electric utilities, such as reduced operational and maintenance costs, increased reliability, and informed decisions for future development [5]. In addition, accurate load forecasting is essential in competitive power markets, where electricity prices are driven by demand, directly affecting the financial performance of market participants [6].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection

Magalhães,

Bento,

Pombo

et al. 2024

Energies

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Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

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Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach

Cited by 29 publications

References 64 publications

Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

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

Short-Term Load Forecasting Based on Optimized Random Forest and Optimal Feature Selection

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