Short-term Power Load Forecasting Research Articles

Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism

This study proposes a new approach for short-term power load forecasting using a combination of convolutional neural networks (CNN), long short-term memory (LSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to enhance the accuracy of short-term power load prediction, which is crucial for efficient energy management. The study analyzes the relationship between the target load and the collected parameters, identifying the most influential factors using Pearson correlation coefficient analysis. A one-dimensional CNN layer is utilized to extract high-dimensional features from the input data, followed by an LSTM layer that captures temporal correlations within the historical sequences. Finally, an attention mechanism is introduced to optimize the weight of the LSTM output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using two benchmark models based on mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The results show that the CNN-LSTM-A model outperforms the traditional LSTM model regarding power load prediction accuracy for two thermal power units, with an improvement of 7.3% and 5.7%, respectively, indicating superior performance. Therefore, this study demonstrates the effectiveness of the proposed CNN-LSTM-A model for short-term power load forecasting, which has potential applications in the energy industry. In conclusion, the proposed approach can improve the accuracy of power load forecasting, leading to more efficient energy management and cost savings. Additionally, the study highlights the importance of incorporating attention mechanisms into traditional LSTM models for power load forecasting, as it helps to optimize the weight of the LSTM output and improve the accuracy of the predictions. The proposed CNN-LSTM-A model can be potentially useful for energy companies and policymakers in making informed decisions regarding energy production and consumption. Overall, this study provides a valuable contribution to power load forecasting, and the proposed approach could be extended to other areas of time-series forecasting in the future.

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.

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 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.

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.

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 BES-VMD and CNN-Bi-LSTM method with error correction

Aiming at the strong non-linear and non-stationary characteristics of power load, a short-term power load forecasting method based on bald eagle search (BES) optimization variational mode decomposition (VMD), convolutional bi-directional long short-term memory (CNN-Bi-LSTM) network and considering error correction is studied to improve the accuracy of load forecasting. Firstly, a decomposition loss evaluation criterion is established, and the VMD optimal decomposition parameters under the evaluation criterion are determined based on BES to improve the decomposition quality of the signal. Then, the original load sequence is decomposed into different modal components, and the corresponding CNN-Bi-LSTM network prediction models are established for each modal component. In addition, considering the influence of various modal components, holiday and meteorological factors on the error, an error correction model considering short-term factors is established to mine the hidden information contained in the error to reduce the inherent error of the model. Finally, the proposed method is applied to a public dataset provided by a public utility in the United States. The results show that this method can better track the changes of load and effectively improve the accuracy of short-term power load forecasting.

Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant information from different time steps to reduce the prediction error of the model. Simultaneously, the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism, so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input. Finally, the chaotic sparrow search algorithm (CSSA) is used to optimize the hyperparameter selection of the model. The short-term power load forecasting on different data sets shows that the average absolute errors of short-term power load forecasting based on our method are 0.876 and 4.238, respectively, which is lower than other forecasting methods, demonstrating the accuracy and stability of our model.

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network.

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm.

Short-term power load forecasting is essential in ensuring the safe operation of power systems and a prerequisite in building automated power systems. Short-term power load demonstrates substantial volatility because of the effect of various factors, such as temperature and weather conditions. However, the traditional short-term power load forecasting method ignores the influence of various factors on the load and presents problems of limited nonlinear mapping ability and weak generalization ability to unknown data. Therefore, a short-term power load forecasting method based on GRA and ABC-SVM is proposed in this study. First, the Pearson correlation coefficient method is used to select critical influencing factors. Second, the gray relational analysis (GRA) method is utilized to screen similar days in the history, construct a rough set of similar days, perform K-means clustering on the rough sets of similar days, and further construct the set of similar days. The artificial bee colony (ABC) algorithm is then utilized to optimize penalty coefficient and kernel function parameters of the support vector machine (SVM). Finally, the above method is applied on the basis of actual load data in Nanjing for simulation verification, and the results show the effectiveness of the proposed method.

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

Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error.

An ensemble multi-step M-RMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting

Accurate prediction of the power load is one of the keys to guarantee stable operation of power construction. However, with the surge of power load, the uncertainty of multi-step prediction brings more challenges. To reduce the uncertainty of multi-step short-term power load forecasting, a short-term ensemble day-ahead forecasting model based on variational modal decomposition (VMD) and two-group multi-step strategy is proposed in this paper. Firstly, VMD is used to decompose the original sequence into different sub-sequences. Then, according to the curve characteristics of different sub-sequences, different multi-step strategies are selected to achieve the best prediction effect. Finally, the multi-step results of all sub-sequences are integrated to get the final day-ahead prediction results. Among them, the two groups of multi-step strategies are divided into single-output approaches (Direct, Recursive, DirRec) and multi-output approaches (MIMO, RecMO, DirMO). For the prediction tool of the single-output approaches, a hybrid prediction model with integrating decomposition, feature selection, optimization algorithm and LSSVR is proposed. For the prediction tool of the multi-output approaches, a hybrid algorithm based on multi-kernel RMLSSVR is proposed to enhance the diversity of MLSSVR, and the parameters and weights of multi-kernel MLSSVR are optimized by the PSO with extreme value disturbance without velocity update. Taking the hourly market load data of California as an example, three groups of comparative experiments are carried out on the proposed method. Experiments show that the accuracy of our model is improved by at least 19% compared with the best RecMO strategy.

Research on Short-term Load Forecasting Based on GWO-BILSTM

In short-term power load forecasting, changes in external multi-dimensional factors will have a certain impact on the accuracy of load forecasting. In response to this problem, this paper proposes a prediction model GWO-BILSTM that combines the Grey Wolf Optimizer (GWO) and the bi-directional long short-term memory (BILSTM) network. Taking the real power load data as the data set, the high correlation parameters are selected as the input through the Pearson correlation analysis, the hyperparameters of BILSTM are optimized by the GWO algorithm, and finally the GWO-BILSTM prediction model is established based on the optimized parameters to predict the data set. The experimental results show that the mean absolute percentage error, root mean square error and mean absolute error index of the GWO-BILSTM model are better than other comparison models, which effectively improves the prediction accuracy of multi-dimensional power load data.

Short-term power load probability density forecasting based on GLRQ-Stacking ensemble learning method

The high penetration rate of distributed energy brings severe challenges to the dispatch and operation of power systems. Improving the accuracy of short-term power load forecasting can enhance the management efficiency of the system, and thus enhance the economic and social benefits of the system. To that end, gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and random forest (RF) are used as the base learners, and quantile regression long-short term memory (QRLSTM) is used as the meta-learner to construct a novel Stacking ensemble learning model (GLRQ-Stacking) in this paper. This is a novel heterogeneous ensemble framework, which cleverly combines Bagging and Boosting to achieve Stacking. Thereinto, more comprehensive forecast information is provided by QRLSTM. Afterwards, a density prediction method using Gaussian approximation of quantiles (GAQ) to modify kernel density estimation (KDE) is proposed. The reconstructed probability density curves are derived by modifying KDE. While unifying the values of metrics, the constructed prediction intervals (PIs) cover more real values. The accuracy and effectiveness of the framework are evaluated based on two German datasets from different seasons and one Electrician Mathematical Contest in Modeling (EMCM) data case. Through the comparison of the results, the superiority of the proposed ensemble framework and the modified density prediction method is verified, which could improve operations management of power systems.

Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory

Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory

Multivariate Adaptive Step Fruit Fly Optimization Algorithm Optimized Generalized Regression Neural Network for Short-Term Power Load Forecasting

Short-term load forecasting plays a significant role in the management of power plants. In this paper, we propose a multivariate adaptive step fruit fly optimization algorithm (MAFOA) to optimize the smoothing parameter of the generalized regression neural network (GRNN) in the short-term power load forecasting. In addition, due to the substantial impact of some external factors including temperature, weather types, and date types on the short-term power load, we take these factors into account and propose an efficient interval partition technique to handle the unstructured data. To verify the performance of MAFOA-GRNN, the power load data are used for empirical analysis in Wuhan City, China. The empirical results demonstrate that the forecasting accuracy of the MAFOA applied to the GRNN outperforms the benchmark methods.

Short-Term Power Load Forecasting of Integrated Energy System Based on Attention-CNN-DBILSTM

In view of the fact that the potential high-dimensional features in the historical sequence are difficult to be effectively extracted by traditional power load forecasting methods and the coupling factors of electricity, heat, and gas have not been considered, the correlation of electric heating and gas load is considered in this paper, and a short-term power load forecasting method for integrated energy systems based on Attention-CNN- (Convolutional Neural Network-) DBILSTM (Deep Bidirectional Long-Short-Term Memory) is proposed. First, the correlation between the multiple load influencing factors is considered, and the Pearson coefficient is used to quantitatively calculate the correlation between the multiple loads. Second, a CNN network consisting of a one-dimensional convolutional layer and a pooling layer is established. High-dimensional features reflecting the dynamic changes of the load are extracted, and the proposed feature vector is constructed in the form of time series as the input of the DBILSTM network; the dynamic change law of time series data is modeled and learned. Then, the Attention mechanism is introduced to assign different weights to the hidden state of DBILSTM through the mapping weight and the learning parameter matrix, to reduce the loss of historical information and strengthen the impact of key information, and the Dense layer is used to output the load prediction results. Finally, the influence of the correlation of multiple loads and its influencing factors on the power load forecasting results is analyzed, based on the historical load data of the integrated energy system in a certain area of Northeast China. The simulation results of the calculation example show that the prediction accuracy of the method reaches 97.99%, and the integrated energy system electric, heat, and gas load correlation coefficients as the input parameters of the Attention-CNN-DBILSTM network can reduce the average prediction error by 0.37%∼1.93%. The proposed method has been verified to effectively improve the prediction accuracy by comparison with the prediction model results of CNN-LSTM network, CNN-BILSTM network, and CNN-DBILSTM network.

Building’s hourly electrical load prediction based on data clustering and ensemble learning strategy

The short-term power load forecasting of buildings plays a key role in peak demand management and power generation scheduling. Many studies have shown that ensemble learning strategies are more accurate and feasible in practice than individual predictive models. However, the common way processing massive data is still traditional, that is, training and testing them as a whole, ignoring data’s inter law and pattern difference, which probably hinders the further improvement of forecasting effect. In this study, the characteristics of building data are identified and classified before model construction. Fully considering the diversities of model structure and parameters, an adaptive ensemble learning strategy is proposed for short-term building electrical loads forecasting. The unsupervised K-means clustering and supervised KNN (K-nearest neighbors) classification methods are combined for data clustering. Total eleven different sub-learners are applied for ensemble learning and a variety of intelligent optimization algorithms are used for parameter adjusting. The overall prediction accuracy is derived through a fusion module. Two sets of short-term electrical load data of actual buildings are used for model verification. The results show that the data clustering based ensemble learning strategy has better forecasting accuracy compared to eleven individual models and previous reported predictions. The superior prediction accuracies in different two cases also verify the adaptability and generalization abilities of the proposed ensemble method.