The Short‐Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China

Dong, Huanhe; Gao, Ya; Fang, Yong; Liu, Mingshuo; Kong, Yuan

doi:10.1155/2021/3693294

Cited by 19 publications

(8 citation statements)

References 39 publications

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“…In [ 27 ], a study on short-term load forecasting in Qingdao, China utilized Bagged Regression Trees (BRT) and introduced indicator variables for special days like holidays. This approach improved BRT’s performance and outperformed ANN in terms of speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Predictive models for short-term load forecasting in the UK’s electrical grid

Sha’aban

2024

PLoS ONE

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There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK’s power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.

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Section: Introductionmentioning

confidence: 99%

Predictive models for short-term load forecasting in the UK’s electrical grid

Sha’aban

2024

PLoS ONE

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“…On the other hand, Dong et al [ 22 ] developed an hourly electrical load forecasting model based on bootstrap aggregating (Bagging) for Chinese special days. They collected three years of hourly electrical load in Qingdao, China.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist

Moon

Park

Rho

et al. 2022

Computational Intelligence and Neuroscience

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Daily peak load forecasting (DPLF) and total daily load forecasting (TDLF) are essential for optimal power system operation from one day to one week later. This study develops a Cubist-based incremental learning model to perform accurate and interpretable DPLF and TDLF. To this end, we employ time-series cross-validation to effectively reflect recent electrical load trends and patterns when constructing the model. We also analyze variable importance to identify the most crucial factors in the Cubist model. In the experiments, we used two publicly available building datasets and three educational building cluster datasets. The results showed that the proposed model yielded averages of 7.77 and 10.06 in mean absolute percentage error and coefficient of variation of the root mean square error, respectively. We also confirmed that temperature and holiday information are significant external factors, and electrical loads one day and one week ago are significant internal factors.

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“…Furthermore, they perform poorly in terms of accuracy, performance, and speed compared to AI-based methods, especially ML methods used in power system applications. The most popular ML-based methods in STLF are ANNs [13][14][15], support vector machines (SVMs) [16] , regression tree (RTs) [17], MLR [12,18], k-nearest neighbor (KNN) algorithms [19], recurrent neural networks (RNNs) [20,21], convolutional neural networks (CNNs) [22,23], and different ML ensemble models [4,24,25] and hybrid models, which come through deficits of conventional and AI forecasting models [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods

Yaprakdal¹

2022

Energies

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The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term load forecasting (STLF) incorporates hour-ahead load forecasting, which is critical for dynamic data-driven smart power system applications. Nevertheless, based on our knowledge, there are not enough academic studies prepared with particular emphasis on this sub-topic, and none of the related studies evaluate STLF forecasting methods in this regard. As such, machine learning (ML) and deep learning (DL) architectures and forecasters have recently been successfully applied to STLF, and are state-of-the-art techniques in the energy forecasting area. Here, hour-ahead load forecasting methods, the majority of which are frequently preferred high-performing up-to-date methods in the literature, were first examined based on different forecasting techniques using two different aggregated-level datasets and observing the effects of these methods on both. Case and comparison studies have been conducted on these high-performing methods before, but there are not many examples studied using data from two different structures. Although the data used in this study were different from each other in terms of the time step, they also had very different and varied features. In addition, feature selection was studied on both datasets and a backward-eliminated exhaustive approach based on the performance of the artificial neural network (ANN) on the validation set was proposed for the development study of the forecasting models. A new DL-based ensemble approach was proposed after examining the results obtained on two separate datasets by applying the feature selection approach to the working forecasting methods, and the numerical results illustrate that it can significantly improve the forecasting performance compared with these up-to-date methods.

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The Short‐Term Load Forecasting for Special Days Based on Bagged Regression Trees in Qingdao, China

Cited by 19 publications

References 39 publications

Predictive models for short-term load forecasting in the UK’s electrical grid

Predictive models for short-term load forecasting in the UK’s electrical grid

Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist

An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods

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