Short-term Power Load Research Articles

Two-Stage Short-Term Power Load Forecasting Based on SSA–VMD and Feature Selection

Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-influencing factors, this paper proposes a two-stage short-term load forecasting method, SSA–VMD-LSTM-MLR-FE (SVLM–FE) based on sparrow search algorithm (SSA), to optimize variational mode decomposition (VMD) and feature engineering (FE). Firstly, an evaluation criterion on the loss of VMD decomposition is proposed, and SSA is used to find the optimal combination of parameters for VMD under this criterion. Secondly, the first stage of forecasting is carried out, and the different components obtained from SSA–VMD are predicted separately, with the high-frequency components input to a long short-term memory network (LSTM) for forecasting and the low-frequency components input to a multiple linear regression model (MLR) for forecasting. Finally, the forecasting values of the components obtained in the first stage are input to the second stage for error correction; factors with a high degree of influence on the load are selected using the Pearson correlation coefficient (PCC) and maximal information coefficient (MIC), and the load value at the moment that has a great influence on the load value at the time to be predicted is selected using autocorrelation function (ACF). The forecasting values of the components are fused with the selected feature values to construct a vector, which is fed into the fully connected layer for forecasting. In this paper, the performance of SVLM–FE is evaluated experimentally on two datasets from two places in China. In Place 1, the RMSE, MAE, and MAPE are 128.169 MW, 102.525 MW, and 1.562%, respectively; in Place 2, the RMSE, MAE, and MAPE are 111.636 MW, 92.291 MW, and 1.426%, respectively. The experimental results show that SVLM–FE has high accuracy and stability.

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

Under the “double carbon” policy, it is time to build a new green and safe power system. Special attention should be paid to the low accuracy in short-term power load prediction. Therefore, this paper presents a technique for anticipating short-term power demand based on a bidirectional long short-term memory network with a whale-optimized attention mechanism (WOA-BiLSTM-Attention), which is used to forecast and analyze the measured power values in a certain area. The experimental findings reveal that the suggested technique has much greater prediction accuracy and convergence speed, as well as superior stability, when compared to LSTM, BiLSTM, and BiLSTM based on attention mechanism, making it a good reference for power system planning and stability.

A Short-term Power Load Forecasting Method Based on An Improved VMD-LSTM Algorithm

Accurate power load prediction is a crucial basis to ensure the balance of power supply and demand. This paper provides a modified variational modal decomposition (VMD) - long short-term memory (LSTM) algorithm, using genetic algorithm (GA) to iteratively optimize the number of decomposition layers and penalty factor parameters, and using VMD to obtain the modal components, fuse them with the filtered features. Then the reconstruction data is obtained through normalization according to the parameters of the original load characteristic data set. Finally, LSTM is constructed to excavate temporal characteristics and obtain power load prediction results. Simulation fully demonstrates the superior performance of this modified model in terms of prediction effect.

Research on short-term load forecasting of power system based on IWOA-KELM

A short-term power load forecasting (STPLF) model based on the Improved Whale Optimization Algorithm (IWOA) optimized Kernel Extreme Learning Machine (KELM) is proposed to address the problems of high randomness and low forecasting accuracy of electricity loads. The KELM model is constructed, and the IWOA is used to optimize the core and penalty parameters of the KELM to establish the IWOA-KELM electricity load forecasting model. Combined with the actual data of a certain region, the forecasting analysis results show that the convergence speed and forecasting accuracy of the method are greatly improved compared with IWOA-BP, IWOA-SVM and IWOA-ELM forecasting methods.

Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples

There are many influencing factors present in different situations of power load. There is also a strong imbalance in the number of load samples. In addition to examining the problem of low training efficiency of existing algorithms, this paper proposes a short-term power load prediction method based on feature selection and error compensation under imbalanced samples. After clustering the load data, we expand some sample data to balance the sample categories and input the load data and filtered feature sequences into the improved GRU for prediction. At the same time, the errors generated during the training process are used as training data. An error correction model is constructed and trained, and the results are used for error compensation to further improve prediction accuracy. The experimental results show that the overall prediction accuracy of the model has increased by 80.24%. After expanding a few samples, the prediction accuracy of the region where the samples are located increased by 59.41%. Meanwhile, due to the improvement of the algorithms, the running time was reduced by approximately 14.92%.

Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning

Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method.

Optimized Seq2Seq model based on multiple methods for short-term power load forecasting

Accurate power load prediction plays a key role in reducing resource waste and ensuring stable and safe operations of power systems. To address the problems of poor stability and unsatisfactory prediction accuracy of existing prediction methods, in this paper, we propose a novel approach for short-term power load prediction by improving the sequence to sequence (Seq2Seq) model based on bidirectional long-short term memory (Bi-LSTM) network. Different from existing prediction models, we apply convolutional neural network, attention mechanism, and Bayesian optimization for the improvement of the Seq2Seq model. Moreover, in the data processing stage, we use the random forest algorithm for feature selection, and also adopt the weighted grey relational projection algorithm for holiday load processing to process the data and thereby overcome the difficulty of holiday load prediction. To validate our model, we choose the power load dataset in Singapore and Switzerland as experimental data and compare our prediction results with those by other models to show that our method can generate a higher prediction accuracy.

Combined machine learning forecasting method for short-term power load based on the dynamic weight adjustment

The load of power system exhibits evident characteristics of volatility and randomness. The traditional load forecasting algorithm usually studies and trains the historical data to obtain the load model, which makes it difficult to adapt to the load dynamic change situation, and then resulting the unreasonable inaccurate prediction. In this paper, a combinatorial machine learning model is adopted to forecast short-term power load using a dynamic adjustable weight. Firstly, a combined machine learning model is constructed using three types of algorithms including the improved long and short-term neural network, bagging algorithm, and boosting regression algorithm. The weight of each algorithm is determined dynamically by the improved error function. Secondly, the dynamic error function and the optimal weight optimization algorithm are employed so as to balance the contradiction between the speed and accuracy of dynamic adjustment. For different months or different days within a month, different weight adjustment algorithms are selected for enhancement. In addition, a penalty term is introduced to improve the algorithm accuracy and the final prediction outcomes. Finally, a practical load prediction case is simulated and compared with the traditional combined prediction model with fixed weights. It is verified that the proposed model can effectively eliminate the excessive errors caused by the poor dynamic response effect. It has a good dynamic response effect and accurate prediction. The error rate is only 1.24% when the load fluctuation is significant. This study provides a novel approach to forecasting short-term power load.

An improved hybrid model for short term power load prediction

An improved hybrid model for short term power load prediction

Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model

Accurate short-term load forecasting is the key to ensuring smooth and efficient power system operation and power market dispatch planning. However, the nonlinear, non-stationary, and time series nature of load sequences makes load forecasting difficult. To address these problems, this paper proposes a short-term load forecasting method (EPT-VMD-TCN-TPA) based on the hybrid decomposition of load sequences, which combines ensemble patch transform (EPT), variational modal decomposition (VMD), a temporal convolutional network (TCN), and a temporal pattern attention mechanism (TPA). In which, the trend component (Tr(t)) and the residual fluctuation component (Re(t)) of the load series are extracted using EPT, and then the Re(t) component is decomposed into intrinsic modal function components (IMFs) of different frequencies using VMD. The Tr(t) and IMFs components of the fused meteorological data are predicted separately by the TCN-TPA prediction model, and finally, the prediction results of each component are reconstructed and superimposed to obtain the final predicted value of the load. In addition, experiments after reconstructing each IMF component according to the fuzzy entropy (FE) values are discussed in this paper. To evaluate the performance of the proposed method in this paper, we used datasets from two Areas of the 9th Mathematical Modeling Contest in China. The experimental results show that the predictive precision of the EPT-VMD-TCN-TPA model outperforms other comparative models. More specifically, the experimental results of the EPT-VMD-TCN-TPA method had a MAPE of 1.25% and 1.58% on Area 1 and Area 2 test sets, respectively.

Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series

This article introduces a carefully-engineered forecasting methodology for day-ahead electric power load forecasts evaluated using the European Network of Transmission System Operators for Electricity (ENTSO-E). Two steps were employed to configure the desired forecasting methodology: First, a straightforward processing pipeline is proposed to enable systematic preprocessing of raw multivariate time-discrete power data extracted from the ENTSO-E repository, including a stride-based sliding window approach to generate time series-based batches ready for the supervised learning procedure. Second, the lightweight type of recurrent neural network method, namely gated recurrent units (GRU), is selected and carefully calibrated to yield accurate multi-step forecasts, which was trained using the preprocessed multivariate time series data to render day-ahead power load forecasts. The forecasting estimates generated by the proposed GRU model are evaluated using a set of regression-based metrics to assess the models’ precisions. The empirical results show that the proposed forecasting methodology yields outstanding day-ahead power load forecasting performance regarding the enterprise-class measured data compared to a statistical model, namely autoregressive integrated moving average with exogenous variables (ARIMAX), as well as the actual day-ahead forecasts generated by the ENTSO-E platform.

Short-term power load forecasting based on RF-CNN-SVM

In order to reduce the error of short-term power load forecasting and improve its forecasting accuracy, A prediction method based on the combination of random forest (RF), convolution neural network (CNN) and support vector machine (SVM) is proposed. First, the data is preprocessed, and the RF algorithm is introduced to optimize the input variables, Then the feature is extracted through CNN, Finally, the extracted results are input into the SVM model, and the forecasting results are output to realize the load forecasting. In this paper, the power load data of Singapore is used for experimental analysis, compared with CNN-SVM model without RF algorithm, SVM model and hybrid model of convolutional neural network and long short-term memory network (CNN-LSTM), The results show that the prediction model method proposed in this paper has better prediction effect.

Stacking strategy-assisted random forest algorithm and its application

Short-term power load forecasting provides important guidance for the improvement of power marketing and control levels of power enterprises. In this paper, a novel method, named RF-TStacking, is proposed to forecast the short-term load. This study starts from the influence factors of the power load, the random forest is applied to estimate the importance of the influence factors of short-term load. Based on Stacking strategy, the integration of LightGBM and random forest is realized to achieve short-term power load forecasting. To improve the generalization ability of the load model, random put back sampling is used to sample each primary learner, and the average value is taken as the result of each primary learner. The Bayesian optimization is used to adjust the super parameters of the model to improve the accuracy of the selection of influencing factors. The load data of a region in northwest China are used for the testing, and it is found that the model can provide stable prediction results.

A Platform Independent Web-Application for Short-Term Electric Power Load Forecasting on a 33/11 kV Substation Using Regression Model

Short-term electric power load forecasting is a critical and essential task for utilities of the electric power industry for proper energy trading and that enable the independent system operator to operate the network without any technical and economical issues. In this paper, machine learning model such as linear regression model is used to forecast the active power load one hour and one day ahead. Real time active power load data to train and test the machine learning model is collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that linear regression model can forecast the load with less mean absolute error i.e. 0.042 with training data and 0.045 with testing data in comparison with support vector regressor model for an hour ahead operation. Whereas in the case of the day ahead operation, linear regression model can forecast the load with less mean absolute error i.e. 0.055 with training data and 0.057 with testing data in comparison with support vector regressor model. A platform independent web application is developed to help the operators of the 33/11 kV substation which is located in Godishala, Telangana State, India.

Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition

With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models.

Two-Stage Short-Term Power Load Forecasting Based on RFECV Feature Selection Algorithm and a TCN–ECA–LSTM Neural Network

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model.

Short Term Power Load Forecasting Based on PSVMD-CGA Model

Short-term power load forecasting is critical for ensuring power system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow algorithm to optimize variational mode decomposition (PSVMD–CGA) is proposed to address the problem of the effect of random load fluctuations on the accuracy of short-term load forecasting. To avoid manual selection of VMD parameters, the Sparrow algorithm is adopted to optimize VMD by decomposing short-term power load data into multiple subsequences, thus significantly reducing the volatility of load data. Subsequently, the CNN (Convolution Neural Network) is introduced to address the fact that the GRU (Gated Recurrent Unit) is difficult to use to extract high-dimensional power load features. Finally, the attention mechanism is selected to address the fact that when the data sequence is too long, important information cannot be weighted highly. On the basis of the original GRU model, the PSVMD–CGA model suggested in this paper has been considerably enhanced. MAE has dropped by 288.8%, MAPE has dropped by 3.46%, RMSE has dropped by 326.1 MW, and R2 has risen to 0.99. At the same time, various evaluation indicators show that the PSVMD–CGA model outperforms the SSA–VMD–CGA and GA–VMD–CGA models.

Short-term Power Load Forecasting Based on Phase Space Reconstruction and EMD-ELM

Short-term Power Load Forecasting Based on Phase Space Reconstruction and EMD-ELM

Research on short-term power load forecasting method based on multi-factor feature analysis and LSTM

In order to improve the accuracy of short-term power load forecasting and fully consider the influence of weather factors on power load, a short-term power load forecasting model based on multi-factor analysis and Long-Short Term Memory (LSTM) neural network is proposed. Firstly, the correlation between different weather factors and load is analysed using the Spearman coefficient method to extract the weather features that have a greater impact on power load. Then the original time series data are reconstructed using the sliding window method. Finally, the forecasting model is established by using LSTM. The proposed model is validated by using the power load data from the 2016 Electrician’s Cup modelling competition, and compared with other models. The results show that the average absolute percentage error of the forecasting model proposed in this paper reaches 7.41% and the average absolute value error is 380.67 MW, which is better than the other models mentioned in the paper.

Short-term Power Load Forecasting Based on Orthogonal PCA-LPP Dimension Reduction and IGWO-BiLSTM

Background: Accurate power load forecasting is of great significance in ensuring power load planning, reliability and economic operation. The traditional power load is easy to be affected by climate, environment, date type and other factors, resulting in the problem of poor forecasting accuracy. Therefore, it is necessary to study power load forecasting. Objective: Through machine learning, dimension reduction method and intelligent optimization algorithm, the accuracy of load forecasting is improved Methods: In order to fully extract load information and improve the accuracy of short-term load forecasting for campus electricity, an improved combination of orthogonal dimensionality reduction and Bilstm is proposed to optimize the hyperparameters in BiLSTM using an improved gray wolf algorithm. Firstly, using the advantages of principal component analysis (PCA) and Locality Preserving Projection (LPP) to maintain the global and local structure of the data, respectively, the Orthogonal PCA-LPP（OPCA-LPP） dimensionality reduction method is proposed to reduce the dimensionality of the original multidimensional data. Finally, the dimensionality-reduced data is used as the input of BiLSTM and optimized by the improved Gray Wolf algorithm, which can enhance the prediction capability of the model and thus achieve accurate prediction of short-term electric load. Results: The Mae and RMSE of this paper are 1.6585 and 1.7602 respectively. The results show that the method proposed in this paper is reasonable Conclusion: This method is applied to power load forecasting. The comparative experimental results show that this method reduces the dimension of data input, simplifies the complexity of network input data, and improves the accuracy of load forecasting. Compared with other methods, it can effectively improve the accuracy of load forecasting, and provide a basis for formulating reasonable power grid operation mode and balanced dispatching of power grid.