Load Forecasting Of Power System Research Articles

A fuzzy adaptive symbiotic organism search based hybrid wavelet transform-extreme learning machine model for load forecasting of power system: a case study

A fuzzy adaptive symbiotic organism search based hybrid wavelet transform-extreme learning machine model for load forecasting of power system: a case study

Application of LSTM-LightGBM Nonlinear Combined Model to Power Load Forecasting

The accurate prediction of power system load is extremely important for the operation of the power market and the safe operation of the power grid. In order to improve the accuracy of short-term load forecasting of power systems, a combination model based on long short term memory network (LSTM) and light gradient boosting machine (LightGBM) is proposed. The experiment first decomposes historical load data by EMD, uses historical weather data and load data decomposed by EMD to establish LSTM prediction model and LightGBM prediction model respectively, and then these two predicted values are linearly combined to obtain the final predicted value. The electrical load data of the 2016 Electrician Mathematical Contest in Modeling is used as an example to verify. The experimental results show that the LSTM-LightGBM combined model has higher forecasting accuracy and application prospects for power load forecasting than traditional load forecasting methods and standard LSTM and LightGBM load forecasting methods.

Research on short-term load forecasting of power system based on gradient lifting tree

Research on short-term load forecasting of power system based on gradient lifting tree

A Holistic Feature Selection Method for Enhanced Short-term Load Forecasting of Power System

Short-term load forecasting (STLF) is important for the operational security and economics of power system. However, most of STLF methods lack an efficient feature selection method to model the time series nonlinearities and feature interaction. In this paper, a new holistic feature selection method is presented. The feedforward long short-term memory network (F-LSTM) is proposed to learn the nonlinear mapping function between features and load. Then, a feature importance matrix is designed to reflect relevancy, redundancy and interaction among the candidate features. Moreover, a hybrid filter-wrapper approach is developed to select suitable features efficiently. The filter part separates useless information for the trained F-LSTM output. The wrapper part selects the optimal subset by fine-tuning the threshold. The results from an empirical study in Switzerland suggest that: 1) the selected subset of features shows high relevancy, low redundancy and high interaction, which is also consistent with the features selected by other feature selection methods, and 2) the proposed method has good prediction performance and can be applied to various artificial neutral network based short-term load forecasting models, which delivers an average 12.1% improvement.

Short-term Load Forecasting System for Power System Based on Big Data

Short-term load forecasting of power system is an important task of power distribution system. Accurate short-term load forecasting provides the best configuration for grid power generation and distribution, maximizing energy saving and ensuring stable operation. This paper aims to study the design of short-term load forecasting system of power system based on big data. On the basis of analyzing power system load forecasting algorithms, classification of load forecasting, characteristics of load forecasting and system design principles, each module of the system is designed in detail, and finally tested the performance of the system. The test results show that the system has no adverse reactions in the use of a large number of users and repeated operation for a long time. In the case of large throughput, the system has a satisfactory response time and relatively reliable system stability.

A Short-Term Electricity Forecasting Scheme Based on Combined GRU Model with STL Decomposition

With development of smart grid, the stable operation of grid has put forward higher requirements for system dispatch. In particular, short-term load forecasting of power systems is a key factor of power grid management systems, which is related to the safety, economy, and stable operation of the smart grid. However, short-term electricity forecasting is affected by many external factors. It has complex characteristics, especially non-linear relationships, so it cannot be accurately predicted. Recently, Recurrent Neural Network based models have good performance in electricity forecasting because of their excellent ability to capture non-linear relationships. However, they cannot fully capture historical information, especially local historical information, which has an impact on prediction accuracy. In order to address these problems, we propose a scheme by combining STL decomposition and GRU model. Specifically, we first decompose the original time series into three different components by STL. The decomposition results are separately imported into the main prediction module, which uses two GRU networks with different structures to obtain the local and global dependencies of the data. We also add an autoregressive method to make the model more robust. The proposed scheme is validated based on real-world data, and the simulation results show that our proposed method can perfectly capture local and global information and achieve higher prediction accuracy than traditional models.

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems

With the advancement of power market reform, accurate load forecasting can ensure the stable operation of power systems increasingly. The randomness of feature change such as climate and day type increases the complexity of short-term load forecasting. To simplify the data processing process to facilitate the practical application and predict short-term loads more accurately, this paper takes the past load data as a feature and considers the time series characteristics of load data simultaneously. The multi-temporal-spatial-scale method is applied to process the load data by reducing the load data noise error and enhancing the time series characteristics. Then, a novel short-term load forecasting model, which is named a multi-temporal-spatial-scale temporal convolutional network, is applied to load forecasting tasks in this paper. The proposed approach can learn the nonlinear feature and time series characteristics of load data simultaneously. To predict the power load of a city in Guangxi Zhuang Autonomous Region (China) in the next day and the next week, the forecasting model is trained by the historical feature load of 7 days, 21 days, 99 days, and 199 days. Compared with 22 artificial intelligent short-term load forecasting models, such as backpropagation neural network and bagging regression, the simulation results show that the proposed multi-temporal-spatial-scale temporal convolutional network can obtain higher accuracy for the short-term load forecasting of power systems than other compared methods.

Short-Term Load Forecasting of Power System Based on Improved BP Neural Network

Power system load is a stochastic and non-stationary process. Due to the influence of various factors, some bad data may exist in the load observation value. These data are mixed into the normal load data to participate in the training of neural network, which seriously affects the accuracy of load forecasting. Short-term load forecasting is the basis of power system operation and analysis, improving the precision of load forecasting is an important means to ensure the scientific decision-making of power system optimization. In order to improve the precision of short term load forecasting in power system, a short-term load forecasting model based on genetic algorithm is proposed to optimize BP neural network. Firstly, using genetic algorithm to optimize the initial weights and thresholds of BP neural network to improve the prediction accuracy of BP neural network; Through the comparison and analysis before and after the model optimization, the experimental results with smaller prediction error were obtained. The simulation results show that the short-term load forecasting model established by this method has faster convergence rate and higher prediction precision.

The Grey Forecasting Model for the Medium-and Long-Term Load Forecasting

Load forecasting plays a particularly significant role in the stable operation of grid and the reasonable distribution of power resources. Based on grey model theory, this paper carries out load forecast of power system. In this paper, the grey model theory is used to realize the medium and long-term load forecasting, and the accuracy of the model to the load forecasting is tested by using the posterior difference method. Finally, an example is given to check the method’s practicability.

Retracted: Short-Term Load Forecasting of Power System based on Neural Network Intelligent Algorithm

Retracted: Short-Term Load Forecasting of Power System based on Neural Network Intelligent Algorithm

Short‐Term Load Forecasting Based on Gaussian Process Regression with Density Peak Clustering and Information Sharing Antlion Optimizer

Short‐term load forecasting is essential for reliable and economical operation of the power system. Gaussian process regression (GPR), as a novel machine learning prediction algorithm, has become one of the commonly used algorithms in the field of short‐term load prediction because it can fully consider the nonlinear and uncertain characteristics of short‐term load sequence. However, due to the unreasonable selection of training set and the inaccurate acquisition of hyper parameters, the prediction accuracy of GPR decreases. In order to improve the accuracy of power system load forecasting, a GPR prediction model based on density peak clustering (DPC) and information sharing antlion optimizer (ISALO) is proposed. Firstly, the DPC is used to find similar days from the historical load data to construct a more reasonable training set. Then, the ISALO, an improved ALO by introducing information sharing mechanism, is used to optimize the hyper parameters of the GPR. Experiments show that the DPC‐ISALO‐GPR model has a 3.33 and 1.22% reduction in mean absolute percentage error compared to Back Propagation Neural Network (BP) and support vector machines, which is suitable for engineering practical applications. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India

This paper offers a novel method of power system load forecasting (PSLF) for regional special event days (RSEDs) when the load demand is highly prejudiced by societal considerations like cultural or religious rituals. These rituals abruptly change the consumer behaviors (demand variations) and it makes the load profile of such RSEDs more complex and nonlinear than normal holidays. Therefore, during RSEDs, an accurate PSLF method must integrate these consumer behaviors in the forecasting process. For this purpose, the offered method uses a new concept of similarity. The offered method is based on support vector machine (SVM) hybridized with a new algorithm called grey wolf optimizer (GWO) to access the proper parameter combinations of SVM for PSLF on RSEDs. This research is carried out in Assam and the novel method is designed to forecast electric power load demand in three RSEDs called Rongali Bihu, Durga Puja, and Diwali. The forecasting results of the offered method demonstrate superior accuracy while compared to the traditional method of training the PSLF system on the data of recent holidays. The efficacy of the offered method is upheld by comparing the forecasting performances with four different standard and recent methods namely, SVM, ANN, PSO-SVM and GA-SVM.

Short-term Load Forecasting of PowerSystem Based on Improved Feedforward Neural Network

Short-term load forecasting of power system is the foundation of safe and stable operation of power grid, at present, short-term load forecasting of power system is easily affected by external features, so it is difficult to extract load data features accurately. Aiming at the problem of nonlinear, high-dimensional and poor generalization ability of load data, the short-term load forecasting method based on flower pollination algorithm and feedforward neural network is proposed. Flower pollination algorithm is used to optimize the combination of feedforward neural network weight threshold to reduce the error and improve the robustness of the algorithm. Finally, the prediction results of similar algorithms and those of the proposed method are compared through prediction evaluation indexes. After comparison, it is found that the method in this paper can effectively improve the accuracy of load prediction, which is conducive to increasing the safety and economy of power grid.

Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

Deep Forest Regression for Short-Term Load Forecasting of Power Systems

Deep neural networks of deep learning algorithms can be applied into regressions and classifications. While the regression performances and classification performances of the deep neural networks are depending on the hyper-parameters of the deep neural networks. To mitigate the adverse effect of the hyper-parameters for the deep learning algorithms, this paper proposes deep forest regression for the short-term load forecasting of power systems. Deep forest regression includes two procedures, i.e., multi-grained scanning procedure and cascade forest procedure. These two procedures can be effectively trained by two completely random forests and two random forests with the default configuration. Then, the deep forest regression is applied into the short-term load forecasting of power systems. The forecasting performances of deep forest regression are compared with that of numerous intelligent algorithms and conventional regression algorithms under the model with the data of previous 7-day, 21-day, and 40-day. Besides, the forecasting performances of deep forest regression with different parameters are compared. The numerical results show that the deep forest regression with default configured parameters can increase the accuracy of the short-term forecasting and mitigate the influences of the experiences for the configuration of the hyper-parameters of deep learning model.

Power load prediction method based on kernel extreme learning machine with t-distribution variation bat algorithm

To improve accuracy of power system load forecasting, according to nonlinearity and uncertainty of load sequence, a power load forecasting method combined with kernel extreme learning machine (KELM) and adaptive variation bat optimization algorithm (AMBA) is proposed in this paper. The model determines mutation probability of the current optimal individual according to the population fitness variance and the value of the current optimal solution and the t-distribution variation is performed on the global optimal individual, and bat individual after mutation is subjected to secondary optimization. Then AMBA is used to optimize the network parameters of kernel function extreme learning machine, so that make full use of advantages of fast learning speed and generalization ability of KELM to realize rapid prediction of load. AMBA-KLEM model is used to predict load of Ningxia Power Grid, and the results are compared with ELM model and KELM model. The results show that the model has higher load prediction accuracy.

A Short-term Load Forecasting Method Based on Fuzzy Neural RBF Network Adaptive Control

According to historical load data of the power grid in a certain area, by which analyze this area’s power load characteristic and consider the load forecasting influence factors such as the date type, temperature, weather conditions in the first. In view of the load has a certain objective laws, but which has a lot of randomness and uncertainty, applying one kind new based on the RBF (Radial Basis Function) Neural Fuzzy Inference to carry on short-term load forecasting. By programming with MATLAB to carry on short-term power system load forecasting, carry on the short-term load forecast experiment to the practical grid and draw the forecasting result curves. The result indicated that the RBF Adaptive Neural Fuzzy Inference of the forecast accuracy is satisfied with the verification of this method is effective and practical.

Short‐term load forecasting based on wavelet neural network with adaptive mutation bat optimization algorithm

To improve the accuracy of short‐term load forecasting of power systems, according to the nonlinearity and uncertainty of short‐term load sequence, a short‐term power load forecasting method combined with wavelet neural network (WNN) and adaptive mutation bat optimization algorithm (AMBA), which is based on the variance of the population's fitness, is proposed in this paper. The model determines the mutation probability of the current optimal individual based on the variance of the population's fitness and the current optimal solution, performs Gaussian mutation on the global optimal individual, and carries out the second optimization on the bat individuals after mutation. AMBA is employed to optimize the network parameters of WNN, improving the prediction accuracy of WNN and speeding up its training. Then the AMBA‐WNN forecasting model is built. The AMBA‐WNN model is used to predict short‐term load of a certain city in China as a case study. The results show that the model can effectively improve the accuracy of short‐term load forecasting and has good practical significance. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Application of Grey Model in Power Load Forecasting

Power load forecasting is the basic work of electric power dispatching and planning of power system, Scientific and reasonable short-term power load forecasting can provide a strong basis for power system dispatching and long-term planning. Using the grey control theory to forecast the electric load, the known information from the annual electricity consumption, the original data were analyzed by the method of generating, strengthening law, establishing the differential equation model, make a scientific and accurate prediction of the demand of electricity. This method needs less complex data, does not take into account the distribution of the rule, the operation is simple and accurate, has provided an effective method for the electric power load forecast.