Review of load forecasting based on artificial intelligence methodologies, models, and challenges

Hou, Hui; Liu, Chao; Wang, Qing; Wu, Xixiu; Tang, Jinrui; Shi, Ying; Xie, Changjun

doi:10.1016/j.epsr.2022.108067

Cited by 38 publications

(10 citation statements)

References 55 publications

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“…e charging behavior of each vehicle is difficult to analyze and accurately model. Research approaches for electric vehicle (EV) charging load are currently separated into three different groupings: (i) behavior analysis, (ii) simulation, and (iii) data analysis [8,13,14]. e vehicle travel patterns can be analyzed using Markov chains and other models, such as traffic travel matrices, to build models that reflect the travel patterns of vehicles in a specific area and time period.…”

Section: Related Workmentioning

confidence: 99%

“…Some researchers have developed a real-time dynamic path stochastic simulation based on travel chains and Markov decision processes [13]. is simulation helps researchers circumvent the issue of having to charge electric vehicles at fixed locations at the same time and more accurately reflects the stochastic nature of the spatial movement of electric vehicles in real time.…”

Section: Related Workmentioning

confidence: 99%

“…Others investigate how to optimally plan the pipe network according to the characteristics of the region's load, but they do not take into account how to optimally optimize station selection and capacity within the region's multiple combined cooling, heating, and power systems. When planning the energy station network, the literature cited above takes into account load characteristics; however, EV charging load is not one of those characteristics [13][14][15]. e ability to accurately forecast short-term electric load has a momentous in uence on both the dispatching and planning of electric energy.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the charging load of electric buses is intermittent and temporal because of the short interval between electric buses. As an example, the LSTM neural network model can be used to effectively solve the time scale problem of EV charging and improve the accuracy in load prediction [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

Chang

Tao

Zhang

et al. 2022

Mobile Information Systems

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Electric buses have a significant penetration rate and high charging frequency and amount. Therefore, their charging load has a momentous influence on the power grid’s operation and dispatch. There are important theoretical and practical reasons to study electric bus charging load prediction; however, the intermittent and random charging behavior of buses makes it more difficult to predict charging load predictions, particularly, in real time. To accomplish this, in this paper a WNN (wavelet neural network)-based dynamic load prediction model for charging electric buses is suggested. We start by using distance and shape to group the charging load curve which, in fact, is done with spectral clustering. As a second step, we take into account a wide range of charging load-affecting variables such as temperature and time of day, in order to better train the WNN. Moreover, the charge loads for each cluster are predicted based on model parameters; subsequently, the forecast day’s total charging load is then calculated by summing the prediction results for each cluster; finally, the proposed method is validated using a real city data set. In our empirical evaluation, it has been found that, under various indicators, the proposed method’s ability to precisely forecast the charging load of electric vehicles has significantly improved. In fact, this allows for better guidance of charging user, planning, and expanding the power grid in consideration of electric vehicle charging loads.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

Chang

Tao

Zhang

et al. 2022

Mobile Information Systems

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“…Moreover, this will indirectly affect the end-user, and electricity consumption costs will decrease. Furthermore, the majority of the operationally decisive processes, such as operational energy planning, dynamic electrical energy consumption pricing in deregulated power market environments, generation and maintenance scheduling, energy transactions, optimal unit commitment, and economic power dispatch, need accurate load forecasting [5].…”

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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