Load Forecasting Of Power System Research Articles

Shore Power System Load Forecasting Model based on IDBO algorithm and PCA-BiLSTM Network

Shore power offers convenient and sustainable power services to ships docking in ports, playing a pivotal role in advancing environmental preservation and the effective utilization of resources. Shore power system loads are susceptible to numerous influencing factors. Therefore, it is crucial to identify a highly accurate load forecasting model to facilitate the judicious allocation of power resources. A short-term shore power system load forecasting model based on the improved dung beetle optimization algorithm (IDBO) and principal component analysis (PCA) in conjunction with the enhanced bidirectional long and short-term memory (BiLSTM) network is proposed in this paper. The IDBO algorithm improves the problem of low convergence accuracy and vulnerability to local optima in DBO. This is achieved by population initialization based on cubic chaotic mapping, amalgamating walrus optimization algorithm, and adaptive t-distribution perturbation strategy. The performance of the BiLSTM, IDBO-BiLSTM, and PCA-IDBO-BiLSTM shore power system load forecasting models are compared through case studies, revealing that the proposed PCA-IDBO-BiLSTM model demonstrates superior predictive capabilities.

Analysis of Chaos Fluctuations in Atmospheric Prediction, Fluid Mechanics and Power System Load Forecasting

Analysis of Chaos Fluctuations in Atmospheric Prediction, Fluid Mechanics and Power System Load Forecasting

Medium‐term load forecasting of power system based on BiLSTM and parallel feature extraction network

AbstractWith the diversification of users’ energy demands, accurate load forecasting is an important prerequisite for optimal scheduling and economic operation of the system, but a single‐load forecasting method cannot effectively predict multi‐energy loads accurately. Therefore, this paper proposes a multi‐energy load forecasting method based on bidirectional long short‐term memory (BiLSTM) and parallel feature extraction networks. Firstly, residual network and convolutional block attention module were used to extract the spatial coupling features of multi‐energy load data. Secondly, BiLSTM is used to capture the temporal features and long‐term dependencies in the load data, and the spatial coupling features are fused to obtain non‐linear prediction results. Finally, the non‐linear prediction results and the linear prediction results obtained by using multi‐energy linear regression were linearly superimposed to obtain the final prediction results. In this paper, IES load data of Tempe Campus of Arizona State University was used to verify and compared with several existing methods, and the results showed that Weighted Mean Absolute Percentage Error decreased by more than 20%.

Study on Short-Term Load Combination Forecasting Model Considering Historical Data Interval Construction

In response to the insufficient accuracy of load forecasting in power system and the wide range of intervals, a combined short-term power load forecasting model considering the interval construction of historical data is proposed. First, the data are decomposed into relatively stable subsequences using extreme-point symmetric mode decomposition (ESMD), and the adaptive dispersion entropy (DE) of C–C algorithm is proposed to recombine similar subsequences. Then, periodicity and correlation analysis are used to determine the input set of each reconstructed component, and the hybrid strategy improved equilibrium optimizer (HSIEO) is proposed to optimize the output weights of the deep extreme learning machine (DELM) to obtain the prediction values of different components and the historical data errors, and the historical data intervals are constructed based on the errors of each component. Then, based on the upper and lower bound estimation method (LUBE), the proposed improved objective function is optimized using the HSIEO-DELM to obtain the component prediction intervals, and the optimal prediction intervals are obtained after superposition. Finally, the experimental comparison shows that the proposed algorithm has higher accuracy and better quality of model prediction intervals.

Application of fuzzy support vector regression machine in power load prediction

Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes.

Research on Load Forecasting of Novel Power System Based on Efficient Federated Transfer Learning

The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics.

Near Real-Time Load Forecasting of Power System Using Fuzzy Time Series, Artificial Neural Networks, and Wavelet Transform Models

Due to the increasing usage of electrical power, the size of electrical power system has increased manifold over the years. There is no inventory or buffer from generation to customer; therefore, to provide a reliable and quality electrical energy whenever demanded, power utility engineers require an adequate, efficient, and precise load forecast to meet continuously varying load demands. This article presents the design and analysis of demand forecasting over shorter interval for power system. The fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT) based forecasting is presented and analyzed in this article. The real-time data from Indian utility is collected for forecasting the demand and to check the effectiveness of FTS, ANN, and WT. The various error definitions are used to calculate the accuracy of the proposed techniques, and the application results verify the superiority of WT and ANN over FTS by showing reduced error value with greater accuracy. Additionally, it is watched that wavelet db3, level 3 is discovered to be the most accurate Daubechies wavelet-oriented technique for predicting the demand in comparison to other dbs, and it highly aligns in reducing the error between actual and predicted demand.

Research on short-term load forecasting of new-type power system based on GCN-LSTM considering multiple influencing factors

With the construction of new-type power system under the ”double carbon” target and the increasing diversification of the energy demand of the user side, the short-term load forecasting of power system is facing new challenges. In order to fully exploit the massive information contained in big data, this paper proposed a new short-term load forecasting method for new-type power system considering multiple factors, which based on Graph Convolutional Network (GCN) and Long Short-Term Memory network (LSTM). Spearman rank correlation coefficient was used to analyze the correlation between load and meteorological factors, and a quantitative model including meteorological factors, date factors and regional factors was established. Thus, GCN and LSTM were jointly used to extract the spatial and temporal characteristics of massive data respectively, and finally the short-term power load forecasting was achieved. The public data sets were used for performance verification compared with three comparison models, LSTM, CNN-LSTM and TCN-LSTM. The results show that the proposed method can make full use of the influence of multi-dimensional data, meanwhile improve the load prediction accuracy and training efficiency effectively.

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 Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network

A dynamic fuzzy neural network for short-term load forecasting of the Greek power system is proposed, and an hourly based prediction for the whole year is performed. A DBD-FELF (Dynamic Block-Diagonal Fuzzy Electric Load Forecaster) consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer, which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed via the use of the Fuzzy C-Means clustering algorithm, and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The performance of DBD-FELF is tested via extensive experimental analysis, and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted.

Improved Power System Load Forecasting Using Information on Climate and Social Events

Power system load forecasting is crucial for power system planning, operation, and control, which reduces operational costs and improves economic efficiency. However, the current forecasting techniques, including LSTM and ARIMA models, ignore the influence of important factors like weather conditions, public holidays, and social events on power system load, which may give rise to inaccurate prediction results. To mitigate this issue, the present work makes use of the Mann-Kendall mutation detection algorithm to detect abrupt changes in power system load caused by the factors mentioned above. A correction function is then developed to improve the prediction accuracy of a conventional prediction model like ARIMA. The experimental results validate the effectiveness of the proposed approach.

Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning

Power system load forecasting model plays an important role in all aspects of power system planning, operation and control. Therefore, accurate power load forecasting provides an important guarantee for the stable operation of the power grid system. This paper first analyzes the current status of power system load forecasting, and finds that there are still some deficiencies in the existing forecasting models. In order to make up for these shortcomings, this paper proposes construction of short-term and mid-term power system load forecasting models based on hybrid deep learning. In the data preprocessing part, this paper proposes to use the exponential weight moving average method to process missing values. The detection method of abnormal value is the GeneralizedESDTestAD(GESD). This paper analyzes the historical load data of a regional power grid and four industries, and proposes a short-term power system load forecasting model based on Bi-directional Long Short-Term Memory(BiLSTM); For mid-term load forecasting, this paper first uses random forest and Pearson correlation coefficient to select features. Then a hybrid deep learning model is constructed based on BiLSTM and random forest. After optimizing the parameters of the model, a mid-term power system load forecasting model based on hybrid deep learning is constructed. Finally, the benchmark models are selected for comparative experiments. The experimental results show that the MAPE of the proposed model is 2.36%, which is better than the benchmark models. This proves that the proposed hybrid model can effectively improve the accuracy of power system load forecasting.

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 hybrid temporal convolutional network and Prophet model for power load forecasting

AbstractAccurate and effective power system load forecasting is an important prerequisite for the safe and stable operation of the power grid and the normal production and operation of society. In recent years, convolutional neural networks (CNNs) have been widely used in time series prediction due to their parallel computing and other characteristics, but it is difficult for CNNs to capture the relationship of sequence context and meanwhile, it easily leads to information leakage. To avoid the drawbacks of CNNs, we adopt a temporal convolutional network (TCN), specially designed for time series. TCN combines causal convolution, dilated convolution, and residual connection, and fully considers the causal correlation between historical data and future data. Considering that the power load data has strong periodicity and is greatly influenced by seasons and holidays, we adopt the Prophet model to decompose the load data and fit the trend component, season component, and holiday component. We use TCN and Prophet to forecast the power load data respectively, and then use the least square method to fuse the two models, and make use of their respective advantages to improve the forecasting accuracy. Experiments show that the proposed TCN-Prophet model has a higher prediction accuracy than the classic ARIMA, RNN, LSTM, GRU, and some ensemble models, and can provide more effective decision-making references for power grid departments.

Research on short-term forecasting of power load based on big data BP neural network

The power network system is an indispensable part of the economy development, which directly affects the stable operation of various industries and people’s daily life. During the stable operation of the power system, the prediction of the power load plays an important role in the load scheduling of the power system. Aiming at the problem of short-term load forecasting of power system, this paper established a short-term forecasting model of power load based on the BP neural network forecasting model through the collection of big data and modified the network weights and thresholds through model training. Finally, a short-term prediction of the power load of a certain community was carried out. The results show that the prediction model based on BP neural network can accurately predict the short-term power load with small prediction errors and good prediction performance. It can meet the precision requirements of power system operation scheduling.

An Interval Analysis Scheme Based on Empirical Error and MCMC to Quantify Uncertainty of Wind Speed

The widespread existence of uncertainty makes power system control and decision-making face various risks, especially the interconnection of modern large-scale power systems, which makes its influence range more extensive. In smart grid, the renewable energy prediction and uncertainty analysis methods represented by wind power generation have attracted extensive attention. In this study, an adaptive parameter selection method based on Kullback−Leibler divergence is designed to fully extract the potential information of data. The grey wolf optimization based on reverse learning is used for prediction optimization, identifying historical data, processing dynamic information, and improving the accuracy of the model. In interval prediction, an interval prediction method combining empirical error, quantile statistics, and Markov Chain Monte Carlo is creatively proposed to generate a large number of independent error cases, and form a series of interval predictions with given confidence according to the quantile of random simulation results. Taking wind speed forecasting as an example, it is verified that the method proposed in this article has good performance. Using interval analysis to quantify the volatility and randomness can provide new ideas for load forecasting in power system and renewable energy output forecasting.

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.