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Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Many researches on short-term load forecasting using smart models have been conducted at home and abroad. However, how to obtain the optimal structure and parameters accurately and quickly poses a challenge to short-term load forecasting, because the prediction performance of smart forecasting methods is more easily affected by the structure and parameters of the method, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, a tree-structured Parzen estimator (TPE)-based boosting short-term load forecasting method is proposed. The results show that the proposed method can achieve rapid optimization of structure and parameters, which is verified in the application in short-term load forecasting of a southern province in China to improve the prediction accuracy.

To explore the hidden information in power load data and improve the accuracy of short-term load forecasting, a hybrid prediction model based on variational mode decomposition (VMD), long-term and short-term memory network (LSTM), improved particle swarm optimization (IPSO) algorithm and gated recurrent unit neural network (GRU) was proposed with consideration of strong non-linearity and non-stationarity in power load. First, correlation analysis was employed to determine input factors. The VMD algorithm combined with sample entropy was employed to decompose the load data into a series of intrinsic mode function ( IMF) and residual components, which then allowed for the rational determination of the decomposition levels and penalty factors. Then, these quantities were divided into low-frequency and high-frequency components based on the zero-crossing rate. The low-frequency components were forecasted using an LSTM network, while the high-frequency components were predicted with an IPSO-GRU network. Finally, the predicted results were reconstructed to obtain the final result of power load. Simulation results show that the proposed hybrid prediction model can effectively extract modal features and possesses higher predictive accuracy compared with alternative models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Short-term power load has strong randomness and volatility, in order to improve the accuracy of load forecasting, this paper proposes a combined forecasting model based on quadratic decomposition, convolutional neural (CNN) network and long short-term memory (LSTM) neural network. Firstly, the original load series was decomposed into several intrinsic mode components and residuals by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, the sample entropy and K-means (SE-K-means, SK) were introduced to reconstruct the sub-sequences obtained by decomposition into three sequences, and the strong non-stationary sequences in the reconstructed components were decomposed twice by using variational mode decomposition. A CNN-LSTM model was established to predict each sub-sequence obtained by decomposition. Finally, the forecasting results were superimposed to achieve effective load forecasting. By using the actual load data for verification, it can be seen that from the perspective of four evaluation indicators: R2, mean absolute error, root mean square error and mean absolute percentage error, the proposed model has higher fitting and prediction accuracy than XGBoost, LSTM, CEEMDAN-LSTM and CEEMDAN-CNN-LSTM models.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Residential Short-term load forecasting is an important basis for power department to make dispatching plan reasonably. A short-term load forecasting framework was proposed that is based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering algorithm, Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU). In this framework, based on historical load data collected by smart meters, BIRCH clustering algorithm is adopted to cluster users into multiple user groups according to different users’ electricity consumption habits. A multivariate time series dataset composed of load data, time data, and climate data are into the CNN network, which is composed of a one-dimensional convolutional layer and a pooling layer. The CNN network is responsible for extracting the potential relationship and high dimensional characteristics of different variables. After that, the data are input into the GRU network, and the sequence characteristics of the time sequence data are studied. Finally, the short-term load prediction results are output from the full-connected layer. Taking the open dataset provided by the Commission for Energy Regulation (CER) of Ireland as an example, the Mean Absolute Percentage Error (MAPE) of the proposed method reached 2.932 1%. Compared with the prediction efficiency and accuracy of ANN network, CNN network, and CNN-GRU network under the same conditions, the experimental results showed that the proposed method has higher prediction accuracy and higher training efficiency.

The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the difference in load characteristics and influencing factors in large-scale distribution transformer load forecasting, if all the distribution transformers share a unified model, the prediction accuracy is low, and if the model is built for each distribution transformer, the computational resources will be excessively consumed. An Attention-LSTM short-term forecasting method of distribution load based on multi-dimensional clustering is proposed. The non-parametric kernel method is used to perform probability fitting on the daily load characteristics to form a typical daily load sequence. Improved two-level clustering is applied for load clustering, taking the Euclidean warping distance and influence factors as the similarity evaluation criteria. AP clustering is utilized for obtaining similar time-series, and training sets are formed to train the Attention-LSTM model. Different Attention-LSTM models are obtained by training for different distribution load types and time-series. The effectiveness and practicability of the method proposed are verified by the load data and meteorological data of a municipal distribution network. The accuracy rate is increased by 2.75% and the efficiency is increased by 616.8%.

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to overcome the shortcomings of traditional machine learning algorithms, such as lack of generalization performance and difficulty in determining model parameters and structure, a combined algorithm based on grey correlation and sparrow search algorithm (SSA) was proposed to optimize the parameters of least square support vector machine. The projection principle is used to improve the traditional grey correlation similarity day selection algorithm, and the sparrows search algorithm is used to optimize the parameters of LSSVM. The short-term load forecasting based on the load data of a certain area in south China is compared with Lssvm and particle swarm optimization algorithm (PSO)-LSSVM. The results show that LSSVM optimized by sparrow search algorithm is more accurate, more accurate and more stable than traditional LSSVM and LSSVM optimized by PSO. [ABSTRACT FROM AUTHOR]

To solve the problems that electricity demand was affected by multiple disturbance factors in the electricity market, and the price elasticity of demand was increased, for obtaining more accurate and comprehensive power load forecast value, the cuckoo search algorithm and support vector machine ( CS-SVM) short-term load forecasting method was proposed with considering demand price elasticity. The Pearson correlation coefficient method was used to analyze the load autocorrelation and the correlation among load and historical load temperature, humidity, electricity price, load difference and electricity price difference. Based on the definition of demand price elasticity, a demand price elasticity model was established to reflect the impact of electricity market transactions on load. The cuckoo search algorithm was used to optimize the parameters of support vector machine, and the CS-SVM short-term load forecasting model was established with considering the price elasticity of demand. The actual data of a certain state in the PJM power market in the United States was taken as example to conduct the prediction, and the prediction error was compared with that of comparison model. The results show that the average relative percentage error of the proposed model is 13. 43 %, and the prediction accuracy is improved by 5. 31 % compared with that of the model without introduction of demand price elasticity. [ABSTRACT FROM AUTHOR]

Short-term load forecasting lays the foundation for the economic dispatch of the power system and optimal load distribution of units. Therefore, a short-term load forecasting model combined variational modal decomposition(VMD) and long short-term memory neural network(LSTM) was proposed, and support vector regression(SVR) was used to construct a revised error sequence to compensate the initial prediction sequence. Firstly, the VMD algorithm was used to decompose the non-stationary original load sequence into multiple relatively stable modal components. Then, each modal component was input to the LSTM model for prediction, and the prediction results of each component were combined to obtain the prediction result of VMD-LSTM. Finally, the residual value of the model was input into the SVR model to construct an error sequence to correct the VMD-LSTM load forecast results of the next day. Through actual case tests, the experimental results have lower prediction errors compared with the results of other models, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

At present, researches on short-term load forecasting mainly focus on the analysis of influencing factors and the optimization of the model, and there are few researches on the robustness of the model. This paper took the extreme learning machine as the research object, conducted in-depth research on the robustness of the model, and applied it to the short-term load forecasting problem. The robustness of the ELM model was affected by the loss function. The current ELM model had poor robustness and low prediction accuracy when dealing with the samples containing outliers. To solve this problem, this paper proposed a new loss function based on p-order maximum correntropy criterion. It applied the loss function to the ELM model to improve its robustness in regression prediction. It proposed a method to estimate the percentage of noise in the actual sample, and gave the degree of noise pollution in the sample before established the load forecasting model. The simulation results show that the proposed algorithm model has better robustness and prediction accuracy when the outlier is more than 12%. [ABSTRACT FROM AUTHOR]

Short-term load forecasting plays a central role in the daily operation and dispatching of power systems. Greater flexibility and uncertainty in the operation of power systems have brought harsh requirements on the accurate forecasting of short-term load, particularly with the in-depth advancement of power market reform. A high-precision prediction model is also highly demanding to effectively coordinate the relationship between power generation, transmission, distribution, and consumption. In this study, short-term load forecasting was therefore proposed using variational mode decomposition (VMD) and particle swarm optimization (PSO) modal combination. The VMD was adopted for the adaptive signal decomposition of load sequence, considering the time and frequency domain in the signal decomposition evaluation. The impact of two dimensions on authenticity, independence, and performance was quantitatively clarified, further to determine the evaluation indicators of signal decomposition. An authenticity test of signal decomposition included the redundant component and residual difference component. In redundant components, the Pearson correlation coefficient of the component and the original signal was compared with the authenticity threshold from the perspective of the time domain. In residual components, the ratio of frequency band with significant amplitude in the residual spectrum to the original signal frequency band was used to measure the spectral characteristics of residual components from the perspective of the frequency domain. In the independence index test, two indicators were used to evaluate in the two dimensions of time and frequency domain, including the average Pearson correlation coefficient between the signal components, and the average overlap of significant frequency bands. Time domain was measured using the average Pearson correlation coefficient of each component and the original signal. The average concentration index of the significant frequency band was also selected to verify the signal decomposition. Three indicators were integrated to establish a comprehensive evaluation indicator for signal decomposition. PSO was then used to optimize the parameters of the VMD model, where the comprehensive evaluation index of signal decomposition was taken as the objective function. The influencing factors of daily load included temperature, humidity, historical load, and day-ahead electricity price. The modal combination was used to integrate the modal sequence of influencing factors with similar periodicity in the frequency domain of load. The number of influencing factors after combination was expanded the same as the significant frequency bands of the load frequency domain, where the combined influencing factor modal presented a strong correlation with the load sequence. The expanded variables of influencing factors were input into the PSO-least squares support vector machine (LSSVM) model for the load forecast of the day. The simulation results show that the VMD decomposition using the evaluation index optimization was better than the wavelet analysis, optimized ensemble empirical mode decomposition (EEMD), and non-optimized VMD, indicating the decomposed modal sequence behaved higher quality. Specifically, The maximum relative error increased by 3.36%, the average relative error increased by 1.71%, the maximum absolute error increased by 95MW, and the average absolute error increased by 55.72MW, compared with the PSO-LSSVM using a modal combination. This finding can provide sound support to the construction of a power market with flexible power regulation under the penetration of a high proportion of renewable energy, efficient grid interconnection, and extensive user response. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Short-term load forecasting plays a key role in safe dispatching and economic operation of power system. The parameters of the LSSVM directly affect the prediction effect during the load forecasting accuracy. In order to improve LSSVM load prediction accuracy, a method based on levy adaptive vision artificial fish swarm-shuffled frog leaping algorithm for parameter optimization of LSSVM is proposed. LSSVM is trained by historical data such as load and weather in a certain area. The LAFSA-SFLA-LSSVM forecasting model, the LAVAFSA-SFLA-LSSVM forecasting model and the AFSA-LSSVM forecasting model are established for power load forecasting in a certain area within 24 hours of a certain day. The results show that the accuracy of the LAVAFSA-SFLA-LSSVM forecasting model is higher than the AFSA-LSSVM forecasting model and the LAFSA-SFLA-LSSVM forecasting model, and the prediction error is smaller. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to highlight the randomness, periodicity and related trend of load fluctuation, and further improve the prediction accuracy, this paper proposed a prediction method based on EMD and load fluctuation mechanism. Firstly, it decomposed the EMD of the load to obtain the random component, the periodic component and the trend component. Secondly, it analyzed the correlation between the variation rule of each component and the candidate factor. It deduced the load fluctuation mechanism, and extracted the characteristic value of the load forecasting time. Finally, it de-redundanted the feature. It extracted the novelty of this method was that the time scale characteristic of feature. Taking the load data set of Guangdong Province as a case study, the experimental results show that the proposed method is effective. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of short-term load forecasting, aming at the problem that the forecasting accuracy of traditional single load forecasting model is low and the conventional intelligent algorithm is likely to he trapped into the local optimal problem when solving the high-dimensional and multimodal optimization problems, this paper presented a model which used particle swarm optimization integrated with chaotic crisscross optimization! CC-PSO) to optimize extreme learning machine (ELM) for short-term load forecasting. The generalization ability of ELM was closely related to its input weights and hidden layer biaes, using CC-PSO to optimize the input weights and hidden layer biaes of ELM could improve the generalization ability of ELM and forecasting accuracy. This paper selected and analyzed the actual power grid load data of a certain area in Cuang-dong, the results show that ELM has higher generalization ability and prediction accuracy compared with BP neural networks and support vector machines, CC-PSO has higher global search capability relative to particle swarm optimization and genetic algorithm, and CC-PSO-ELM model has higher load forecasting accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since summer short- term load has the characteristics of fluctuation and nonlinearity, easily affected by temperature, day type and other factors, it is difficult to get the accurate result, relying on single traditional forecasting model. This paper established the ARIMA- SVR combination forecasting model,based on the advantages of ARIMA and SVR, through which the data was linearly fitted. Then the error of ARIMA forecasting was corrected by the SVR forecasting model with the optimized parameters by particle swarm optimization. In the context of the practical cases, the combination forecasting model was used to forecast the trend and to analyze the errors of the summer short- term load. The experimental results indicate that the ARIMA- SVR combination model has higher prediction accuracy than the single model does, which also has a high application value in the forecasting of electricity load. [ABSTRACT FROM AUTHOR]

Urban gas pipeline network short-term load forecasting is important for the security and stability of gas distribution dispatching system. In order to improve the forecast precision, this study adopts an integrated model of empirical mode decomposition, phase space reconstruction and least squares support vector machine, i.e., EMD-PSR-LSSVM, for urban gas pipeline network short-term load forecasting. Firstly, EMD is used to decompose the original nonlinear time series into several uncoupling intrinsic mode functions. Then, PSR is used to make the selection of LSSVM input/output-layer units. Furthermore, particle swarm algorithm is used to optimize the model parameters and train LSSVM with temporal sequence samples, the trained LSSVM will be used for regression forecasting in advanced. Finally, the original loading data of Zhengzhou is adopted as example for empirical analysis. Results indicate that the EMD-PSR-LSSVM model has a higher outcome as compared to BP neural network and LSSVM regression, which has demonstrated the proposed integrated model is efficient and consistent. [ABSTRACT FROM AUTHOR]

A model for short-term load forecasting in power system is developed based on multi-AI (artificial intelligence) methods. Combination algorithms of BP plus fuzzy, SOM plus BP and fuzzy, SOETA plus BP and fuzzy, are applied to forecast the day load of Xiamen. Firstly self-organizing neural network is used to the classification of related data. The historical load data are divided into several kinds, and the certain kind of data is found out, which is as same as that of the forecasting day. Then BP algorithm is used to get basic load forecasting value which takes use of the powerful learning ability and nonlinear reflecting functions. The fuzzy logic is used to construct the function of membership, which can modify the basic load value. Experiment results show that the load forecasting model can acquire satisfactory effect and the combination of SOETA plus BP and fuzzy is the best way, which can improve load forecast accuracy. [ABSTRACT FROM AUTHOR]