Ensemble power load forecasting based on competitive-inhibition selection strategy and deep learning

Luo, Hongyu; Zhang, Haipeng; Wang, Jianzhou

doi:10.1016/j.seta.2021.101940

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…A new wavelet network-optimized re y algorithm is a suggestion that has been made by a number of researchers. e overall performance of all of the models described above is superior to the performance of just one model taken on its own [7,17,20].…”

Section: Related Workmentioning

confidence: 93%

“…e abovementioned literature is able to determine how various factors affect the charging load by simulating a user's travel demand and analyzing the results. On the other hand, due to the randomness and complexity of the model, it is difficult to predict [20]. A number of researchers have proposed a model for the prediction of charging demand.…”

Section: Related Workmentioning

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: 93%

Section: Related Workmentioning

confidence: 99%

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

Chang

Tao

Zhang

et al. 2022

Mobile Information Systems

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“…These complexities arise from diverse electrical loads influenced by weather, calendar factors, diversity of user behaviour and penetration of renewable energy solutions [12]. Due to their direct influence on the daily electricity dispatching that powers both residents' lives and social production activities, STLF and VSTLF have emerged as the central research domains within the field of power load forecasting [13]. Additionally, STLF plays a critical role in guaranteeing the reliability of power systems, particularly during periods of scarcity or outage.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Preprint

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: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

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“…Over the decade, the global energy consumption by the large-scale machinery in factories, buildings, and transport has remarkably increased due to population growth and economic development [1,2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning‐Assisted Short‐Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU

Ullah

Khan

et al. 2022

Complexity

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Over the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy systems’ transition towards more robust and intelligent system will ensure its momentous role in the industrial and economical world. The extraction of deep knowledge from complex energy data patterns requires an efficient and computationally intelligent deep learning-based method to examine the future electricity demand. Stand by this, we propose an intelligent deep learning-based PLF method where at first the data collected from the house through meters are fed into the pre-assessment step. Next, the sequence of refined data is passed into a modified convolutional long short-term memory (ConvLSTM) network that captures the spatiotemporal correlations from the sequence and generates the feature maps. The generated feature map is forward propagated into a deep gated recurrent unit (GRU) network for learning, which provides the final PLF. We experimentally proved that the proposed method revealed promising results using mean square error (MSE) and root mean square error (RMSE) and outperformed state of the art using the competitive power load dataset.(Github Code). (Github code: https://github.com/FathUMinUllah3797/ConvLSTM-Deep_GRU).

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Ensemble power load forecasting based on competitive-inhibition selection strategy and deep learning

Cited by 10 publications

References 26 publications

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

Dynamic Load Prediction Model of Electric Bus Charging Based on WNN

A Hybrid Model Based on LSSVM and the Improved BFOA for Sustainability of Daily Electricity Load Forecasting in Malaysia

Deep Learning‐Assisted Short‐Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU

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