Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

Abumohsen, Mobarak; Owda, Amani Yousef; Owda, Majdi

doi:10.3390/en16052283

Cited by 102 publications

(37 citation statements)

References 53 publications

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“…In order to better verify the superiority of the QCRGRNN algorithm, the RNN model and its variant network LSTM model were established for comparison. Four models of RNN [31], LSTM [32], GRU [33] and QCRGRNN were selected to predict methane production. The prediction results are shown in figure 13.…”

Section: Experimental Comparisonmentioning

confidence: 99%

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Hou,

Che,

et al. 2024

Phys. Scr.

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Methane as a renewable energy source has become a hot topic in recent years. Methane is a bioenergy source produced during the anaerobic digestion of organic waste, and the anaerobic digestion process must be monitored and controlled to produce the required amount of methane in a stable manner. Mathematical modeling is used to simulate digester operation to predict the biogas production from anaerobic digestion, to avoid reactor loading or performance degradation, and to ensure efficient operation of the system. In this paper, a Quantum Convolutional Reconstruction Gated Recurrent Neural Network is proposed. The original data features are extracted by convolutional neural network to reduce the dimensionality and retain the information, the parameterized quantum circuit is integrated in the gating recurrent unit, and the quantum reset gate and quantum update gate are constructed. The information extracted by the Convolution Neural networks is input into the quantum gated recurrent neural network, and the quantum storage unit integrates the information into the hidden layer state, thus processing the hidden layer state information more efficiently. The experimental results show that the prediction accuracy of the A Quantum Convolution Reconstructed Gated Recurrent Neural Network is improved from 81.95% to 88.21%, and the MAE value is reduced from 54.53% to 37.38%.

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Section: Experimental Comparisonmentioning

confidence: 99%

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Hou,

Che,

et al. 2024

Phys. Scr.

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show abstract

“…The strategy involves scheduling these loads to coincide with the availability of RES-like photovoltaic (PV) energy. This approach not only reduces energy consumption costs and promotes sustainability and self-reliance but also augments the penetration of renewable energy [4]. Referred to as energy flexibility (EF), this adaptability is essential for transitioning towards eco-friendly and efficient energy grids.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Shabbir,

Vassiljeva,

Nourollahi Hokmabad

et al. 2024

Electronics

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Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments.

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“…However, this method comes with a more extended training period due to extensive preprocessing and challenges in optimizing hyperparameters. In their study, Abumohsen et al [24] employed RNN, LSTM, and GRU models to conduct STLF using a real-world power system dataset from Palestine. The electrical load dataset was collected from SCADA at one-minute intervals over the course of a year.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

Baul,

Sarker,

Sikder

et al. 2024

BDCC

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Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.

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Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

Cited by 102 publications

References 53 publications

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Prediction of anaerobic digestion performance by quantum convolutional reconstruction gated recurrent neural network*

Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach

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