Short-term Power Load Research Articles

Short-term power load forecasting based on SKDR hybrid model

Short-term power load forecasting based on SKDR hybrid model

Short-term power load forecast using OOA optimized bidirectional long short-term memory network with spectral attention for the frequency domain

Accurate short-term power load forecasting is crucial for maintaining the stability and efficiency of modern power systems, especially in the face of increasing volatility and complexity. This paper introduces a novel approach for short-term power load forecasting by integrating the Osprey Optimization Algorithm (OOA) with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a spectral attention mechanism. The OOA optimizes the hyperparameters of the BiLSTM, improving the model's global search capability and avoiding local optima. The spectral attention mechanism further refines the model by focusing on key frequency components in the time series data. Experimental results demonstrate the superior performance of the proposed model, achieving an RMSE of 0.1382, MAE of 0.0659, and MAPE of 1.14 %, significantly outperforming traditional methods. The model's effectiveness is particularly notable in capturing complex patterns during critical peak and trough periods, making it a valuable tool for enhancing grid operation and management.

A short-term load forecasting method based on QRLSTM considering multiple factors analysis

Abstract Complex characteristics of power loads terribly affect the safety of the power grid. Thus, accurate short-term load forecasting is regarded as a solution to mitigate the effect of load complications. In this regard, this work is putting forward a Long Short-Term Memory Network Quantile Regression (LSTMQR) for power load, obtaining the probability density function (PDF). Firstly, the quantile of future power load was obtained using the LSTMQR model. The conditional quantile obtained by the LSTMQR model is used as the input of the Kernel Density Estimation model (KDE) to predict the future probability density distribution of short-term power load at different prediction times. Furthermore, an assessment method is proposed to quantify the effect of different factors on power load to improve prediction accuracy. Finally, a numerical simulation based on actual data in China is established to validate the effectiveness of the proposed forecasting algorithm, indicating the proposed forecasting algorithm can effectively evaluate the relationship between different factors and power load, and probability density prediction can accurately quantify the uncertainty of load information. The proposed algorithm results in a reduction of 37.6% in forecast error compared to the traditional forecasting algorithm.

Short-Term Power Load Forecasting Method Based on Improved Sparrow Search Algorithm, Variational Mode Decomposition, and Bidirectional Long Short-Term Memory Neural Network

Short-Term Power Load Forecasting Method Based on Improved Sparrow Search Algorithm, Variational Mode Decomposition, and Bidirectional Long Short-Term Memory Neural Network

VMD-BiGRU for Short-term Power Load Forecasting with Energy Valley Optimizer Enhancement

Abstract Electrical load, given context of this modern era, is recognized to have distinct characteristics: diversity, flexibility, and inherently, non-linearity and temporality. Aiming to leverage these characteristics and counteract the challenges within, we propose an electrical load forecasting method, namely EVO-VMD-BiGRU. The proposed technique initiates by utilizing Variational Mode Decomposition (VMD) for the breakdown of the load data into constituent modes, extract the principal modes, and merge them with the features of air temperature, wind speed, precipitation, electricity price and holidays. The modes are then fed to the BiGRU model for training to forecast. The Energy Valley Optimizer (EVO) is applied to search for optimal hyperparameters of the BiGRU model, with the objective of minimizing the RMSE, in order to obtain models for each mode. Finally, the method utilizes these models to forecast the values of each mode, summing them to generate the final predictions. T The efficacy of this approach was confirmed through testing on load datasets sourced from Singapore and Australia. The proposed method achieved an RMSE of 81.20 MW and 30.15 MW on the respective datasets, the MAPE of 0.79% and 0.47%, and the R-squared (R2) of 0.99 and 0.99, which are superior to the alternative methods in the experiments. Results show that the proposed method achieves superior outcomes in short-term electricity load forecasting and exhibits promising potential for practical use.

Short-term power load forecasting in China: A Bi-SATCN neural network model based on VMD-SE.

This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China's "dual-carbon" policy. The integration of renewable energy under this policy has introduced complexities such as nonlinearity and instability. To enhance forecasting accuracy, the VMD-SE-BiSATCN prediction model is proposed. This model improves computational efficiency and reduces prediction errors by analyzing and reconstructing sequence component complexity using sample entropy (SE) following variational mode decomposition (VMD). Additionally, a self-attention mechanism is integrated into the temporal convolutional network (TCN) to overcome the traditional TCN's limitations in capturing long-term dependencies. The model was evaluated using data from the China Ninth Electrical Attribute Modeling Competition and validated with real-world data from a specific county in Shijiazhuang City, Hebei Province, China. Results indicate that the VMD-SE-BiSATCN model outperforms other models, achieving a mean absolute error (MAE) of 92.87, a root mean square error (RMSE) of 126.906, and a mean absolute percentage error (MAPE) of 0.81%.

Multi-area short-term load forecasting based on spatiotemporal graph neural network

Short term power load forecasting can accurately evaluate the overall power load changes and provide accurate reference for power system operation decision-making. To address the limitations of traditional load forecasting methods, which are unable to capture spatial correlations and simultaneously predict load changes in multiple areas, this paper proposes a load forecasting model based on the spatiotemporal attention convolutional mechanism. The proposed model is composed of two key components: a spatiotemporal attention module and a spatiotemporal convolution module. First, the dynamic spatiotemporal correlations between different electricity load areas are captured and analyzed by using the spatiotemporal attention mechanism. Secondly, the spatial pattern and temporal features of the load data sequence are effectively obtained by spatiotemporal convolutional layers. Finally, this paper verifies the accuracy and effectiveness of the proposed method through a real electricity consumption load dataset. The experimental results demonstrate that, in comparison to various benchmark prediction methods, the proposed method can fully explore and utilize the spatiotemporal correlation between different electricity load areas, thereby improving the accuracy of load prediction.

Approach for Short-Term Power Load Prediction Utilizing the ICEEMDAN–LSTM–TCN–Bagging Model

Approach for Short-Term Power Load Prediction Utilizing the ICEEMDAN–LSTM–TCN–Bagging Model

Stochastic configuration networks for short-term power load forecasting

The accuracy of power load forecasting can enhance the operational efficacy and economic efficiency of the power system. This paper utilizes the stochastic configuration network (SCN) and improved marine predator algorithm (IMPA) for power systems short-term load forecasting. First, the daily load data is clustered using the kernel fuzzy C-means (KFCM), and the clustering results are utilized as training input data for the load forecasting model. Then, load forecasting is performed using SCN. Next, IMPA is developed by integrating the opposite-based differential evolution (ODE) and normal mutation perturbation strategy for the purpose of preventing the algorithm from converging to local minima. Finally, IMPA is employed to optimize the critical parameters of the load forecasting model. The experimental results clearly demonstrate that the proposed algorithm performs favorably in terms of the prediction accuracy compared to other modeling algorithms.

Short-term power load forecasting based on Seq2Seq model integrating Bayesian optimization, temporal convolutional network and attention

Power load forecasting is of great significance to the electricity management. However, extant research is insufficient in comprehensively combining data processing and further optimization of existing prediction models. Therefore, this paper propose an improved power load prediction methods from two aspects: data processing and optimization of Sequence to Sequence (Seq2Seq) model. Firstly, in the data processing, Extreme Gradient Boosting (XGBoost) is adopted to eliminate the redundant features for feature extraction. Meanwhile, Successive Variational Mode Decomposition (SVMD) is employed to solve the unsteadiness and nonlinearities present in electricity data during the decomposition process. Secondly, the Seq2Seq model is selected and improved with a variety of machine learning methods. Specifically, input data features are extracted using Convolutional Neural Networks (CNN), enhancing the decoder's focus on vital information with the Attention mechanism (AM). Temporal Convolutional Network (TCN) serves as both the encoder and decoder of Seq2Seq, with further optimization of the model parameters through the Bayesian Optimization (BO) algorithm. Finally, the cases of two real power market datasets in Switzerland and Singapore illustrate the efficiency and superiority of proposed hybrid forecasting method. Through a comprehensive comparison and analysis with the other six models and four commonly used evaluation metrics, it is evident that the proposed method excels in performance, attaining the highest level of prediction accuracy, with the highest accuracy rate of 95.83 %. Consequently, it exhibits significant practical utility in the realm of power load forecasting.

Short-term power load forecasting based on hybrid feature extraction and parallel BiLSTM network

Electricity is a vital resource for societal and economic activities. Accurate electricity load forecasting can effectively reduce costs and enhance energy efficiency. Nevertheless, current research has limitations in effectively extracting and utilizing load forecasting information. This encompasses both undiscovered latent features and models that have not effectively captured long-term dependencies. To address these issues, this paper proposes a novel network architecture, which utilizes a hybrid feature extraction strategy and integrates machine learning methods with deep learning techniques. This method employs a hybrid feature extraction strategy composed of gradient-boosted regression trees, Fisher Score, and mutual information to achieve comprehensive feature representation. The network architecture utilizes a multi-head convolutional neural network and a bi-directional long short-term memory (BiLSTM) with lag parameters. This architecture can simultaneously learn multiple levels and aspects of features, further enhancing the BiLSTM model’s ability to capture long-term dependencies. Through multiple experiments conducted on datasets from Maine, Singapore, New South Wales, Australia, and European countries, our method outperformed the comparison models in three out of the five datasets.

Efficient grid management: smart forecasting of short-term power load using PSO-LSTM

Recent load forecasting techniques combining machine learning models and hyperparameter optimization algorithms have shown success for short-term load forecasting (STLF) task, but they often require complex programming, higher computational costs, and greater parameter tuning. In this paper, we introduce an improved STLF model that combines Long Short-Term Memory (LSTM) neural network with Particle Swarm Optimization (PSO) for enhanced performance. In the proposed approach, the number of hidden neurons in different LSTM layers, learning rate and the number of iterations for training are optimized using the PSO algorithm. To validate the effectiveness of this method, meteorological data and historical load data from a real-world power grid are used as input. The experimental results reveal PSO significantly enhances hyperparameter tuning for LSTM neural networks, leading to improved predictive modelling. The PSO-LSTM model performed better than the LSTM model by more than 20% (in terms of Mean Absolute Error), and showed low sensitivity to hyperparameters. Comparative analysis with alternative approaches from the literature further validates the PSO-LSTM’s effectiveness in STLF. Additionally, the model achieved stable multi-step prediction capabilities, with average errors of 3.6445 for MAE, 4.6509 for RMSE, and 4.6519 for MAPE over a 1–4 day ahead lead times. This study highlights PSO-LSTM’s enhanced robustness and accuracy in power load prediction while addressing hyperparameter tuning challenges through self-optimization.

Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting

Improving the accuracy of electric load forecasting is critical for grid stability, industrial production, and residents' daily lives. Traditional short-term load forecasting methods often struggle to fully capture the long-term dependencies and deep-seated features in unknown datasets, thus limiting their generalization ability. In this paper, we propose an algorithm for short-term power load forecasting based on the stacking integration algorithm of Convolutional Neural Network-Bidirectional Long Short-Term Neural Network-Attention Mechanism (CNN-BiLSTM-Attention) with Extreme Gradient Tree (XGBoost). First, an adaptive hierarchical clustering algorithm (AHC) selects a dataset with similar day characteristics. Then, combined with influencing factors, the Stacking integrated algorithm based on CNN-BiLSTM-Attention and XGBoost is employed for forecasting short-term load data. Finally, the integrated algorithm model was applied to the multi-feature load dataset in the Quanzhou area from 2016 to 2018. Comparative analysis showed that MAPE could be reduced by 5.88–69.40 % in the four selected typical days compared to the comparative algorithm, significantly improving load forecasting accuracy.

A Short-Term Power Load Forecasting Method Based on SBOA–SVMD-TCN–BiLSTM

A Short-Term Power Load Forecasting Method Based on SBOA–SVMD-TCN–BiLSTM

Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention

Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods.

Short-term household power load forecasting based on ICEEMDAN decomposition and CNN-BiLSTM-SA hybrid model

Short-term household power load forecasting based on ICEEMDAN decomposition and CNN-BiLSTM-SA hybrid model

WITHDRAWN: Short-term Load Forecasting Method Based on LSTM Optimized by the Hybrid Dung Bettle Optimization Algorithm

Forecasting electricity load is a crucial responsibility for the power system. Precise short-term power load forecasting (STLF) is a valuable tool for regional production planning, energy conservation, emission reduction, and grid coordination and dispatch. This study suggests a model for STLF that makes use of an optimized Long and Short-Term Memory (LSTM) neural network through the Hybrid Dung Beetle Optimization algorithm (HDBO-LSTM). Firstly, the Hybrid Dung Beetle Optimization (HDBO) algorithm is used to optimize the LSTM network's hyperparameters to address the problem that the LSTM network's random hyperparameter selection will materially influence the accuracy of load forecasting. Secondly, to address the problems that the original Dung Beetle Optimization (DBO) algorithm has poor global exploration ability and is easy to falls into local optimization, multiple improvement strategies are proposed to enhance the DBO algorithm, and the HDBO algorithm, which has a stronger searching ability and faster convergence speed, is proposed. By comparing with several algorithms on 10 benchmark functions, HDBO shows better search performance. Finally, the HDBO-LSTM load forecasting model is developed and evaluated using the Electrotechnical Mathematical Modeling Competition's power load dataset. The outcomes show that HDBO-LSTM outperforms the other comparison models in terms of accuracy and predicting efficacy.

Research on a short-term forecast of industry load based on PSO-LSTM

Abstract Load prediction is crucial to maintaining a stable power grid and encouraging the use of innovative energy types like scenescape energy. The industrial load of a region accounts for a significant proportion of the total load, but the industrial load is difficult to predict due to its large fluctuations and instability. This study proposes a short-term power load prediction model, merging the Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network. The PSO algorithm fine-tunes the LSTM network’s hyperparameters, which are then implemented on the LSTM layer. The trained model, using real-world datasets, is then tested for accuracy. Our model’s effectiveness is backed by practical examples.

Short-term load forecasting of mixed industrial users in distribution networks using DARTS-LSTM method

Abstract Load forecasting has perennially stood as a fundamental pillar within the power industry, with precision in prognostication constituting a paramount necessity for critical functions including grid management, optimization of energy storage, and response to demand fluctuations. In the present study, we introduce a comprehensive global modeling prediction framework aimed at forecasting large-scale power demand within time series data, alongside the development of a novel short-term power load forecasting model. By integrating all time series data pertaining to the same regression task and employing a unified univariate prediction function across the entire time series dataset, we efficaciously forecast large-scale heterogeneous loads aggregated at various levels within distribution networks. The proposed framework leverages the cutting-edge DARTS (Differentiable Architecture Search) deep learning framework for implementation. Through comprehensive experimentation on real-world data, our model framework demonstrates superior performance in terms of overall prediction accuracy, scalability, and robustness in handling missing and non-stationary time series data.

Short-term Power Load Forecasting Based on TCN-BiLSTM-Attention and Multi-feature Fusion

Short-term Power Load Forecasting Based on TCN-BiLSTM-Attention and Multi-feature Fusion