Short-term Power Load Research Articles

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

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

Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting

The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction.There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction.

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.

Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM

Enhancing Short-Term Power Load Forecasting for Industrial and Commercial Buildings: A Hybrid Approach Using TimeGAN, CNN, and LSTM

Short-Term Power Load Forecasting Based on VMD-Pyraformer-Adan

For the characteristics of fluctuation, periodicity and nonlinearity of power load data, this paper proposes a short-term power load forecasting model based on VMD-Pyraformer-Adan. Firstly, the variational modal decomposition (VMD) algorithm is used to modally decompose the electric load data, the over-zero rate and Pearson correlation coefficient are introduced to divide the modal components to obtain the low-frequency, mid-frequency and high-frequency parts, and the reconstructed data are formed with the original load data respectively. Secondly, the reconstructed data are input to the Pyraformer prediction network containing pyramidal attention module (PAM) and coarse-scale construction module (CSCM). Then a new momentum optimizer Adan is used to optimize the parameters of the prediction network. The final output prediction results. The experimental results show that the proposed model in the paper exhibits higher prediction accuracy compared with other models.

Short-Term Power Load Forecasting in FGSM-Bi-LSTM Networks Based on Empirical Wavelet Transform

Short-Term Power Load Forecasting in FGSM-Bi-LSTM Networks Based on Empirical Wavelet Transform

A Parallel Short-Term Power Load Forecasting Method Considering High-Level Elastic Loads

A Parallel Short-Term Power Load Forecasting Method Considering High-Level Elastic Loads

Short-term Power Load Forecasting Based on a New Efficient Deep Learning Framework

Short-term Power Load Forecasting Based on a New Efficient Deep Learning Framework

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.

Short-term power load forecasting method based on improved generalised regression neural network

Short-term power load forecasting method based on improved generalised regression neural network

Short-term power load forecasting method based on improved generalised regression neural network

Short-term power load forecasting method based on improved generalised regression neural network

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

Short-term Power Load Forecasting Based on Improved BP Neural Network from Genetic Algorithm and Simulated Annealing Algorithm

As one of the common methods of load forecasting, the traditional BP neural network algorithm has been favored by scholars for many years due to its strong learning ability and adaptive ability, and is often used by scholars to forecast power load. However, BP neural network also has its own shortcomings, that is, low algorithm efficiency, local minimization and other problems. In this paper, GA algorithm and SA algorithm are used to modify the weights of BP neural network, and a short-term load forecasting model, namely BP-GSA model, is established. The least mean square error prediction model is obtained through training. Then using BP-GSA model and traditional BP neural network model, using MATLAB software to predict the daily power load of a city from July 1 to July 10. The forecast results show that the load forecast curve obtained by BP-GSA model is closer to the actual load curve. That is to say, BP-GSA load forecasting error is less than the traditional BP neural network load forecasting error, which proves that BP-GSA model has better forecasting effect.

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.

Application of Improved Deep Learning Method in Intelligent Power System

In view of the inaccurate short-term power load prediction in the power system, where the smart grid cannot effectively coordinate the production, transportation, and distribution of electric energy, the authors propose the application of improved deep learning methods in intelligent power systems. The method uses the convolutional neural network to establish the energy prediction calculation model, uses CNN adaptive data features to mine characteristics, quantifies power uncertainty, uses drop regularization to optimize the deep network structure, uses the deep forest to learn the extracted data features, and builds a prediction model, in order to achieve accurate prediction of power load and solve the problem that the accuracy of existing forecasting methods decreases due to random fluctuations of power. The results showed the following: in the power load forecast results over the weekend, the random forest and the LSTM algorithm forecast results were relatively close and the RMSEs were 17.3 and 17.1, respectively, while the SVM predicted a larger RMSE error of 27.5. The authors’ method predicts the best with 14.8. Conclusion. After verification based on actual load data, in the case of uncertain fluctuations in power load, this method can accurately predict the power load, and the accuracy is higher than that of the more popular methods at present, and it is expected to become an important technical support for solving the core problems of smart grid.

Distributed energy power prediction of the variational modal decomposition and Gated Recurrent Unit optimization model based on the whale algorithm

Based on the load characteristics of industrial parks, this paper optimizes the load prediction model of industrial parks, in order to provide data support for the research of scheduling algorithms. Aiming at the influence of Variational Modal Decomposition (VMD) modal parameters K and penalty factor α on the prediction accuracy of short-term power load forecasting method based on variational modal decomposition (VMD) and Gated Recurrent Unit (GRU), Whale Optimization Algorithm (WOA) was proposed. In this paper, WOA is used to optimize VMD decomposition parameters. Then, the optimized decomposition parameters decompose the original load data, and a set of more regular modal components are obtained. Finally, each mode decomposed by the WOA-VMD algorithm was sent to GRU for power prediction. And the prediction results were superimposed and reconstructed to obtain the final result. WOA optimized the search process and found more appropriate parameters for better prediction results. After whale algorithm optimization, Root Mean Square Error (RMSE) decreased from 108.8 MW to 38.29 MW, Mean Absolute Error (MAE) decreased from 83.09 MW to 24.26 MW.

Probabilistic short-term power load forecasting based on B-SCN

Grid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision. However, these forecasts are constrained by a number of uncertain factors, which are caused by dynamic environment, the nonlinearity and stochasticity of power demand. To obtain more accurate load forecasting value and quantify the uncertainty effectively, this research proposes a boosting stochastic configuration network(B-SCN) based probabilistic forecasting method. First, correlation analysis is taken in multidimensional input parameters. Second, an adaptive B-SCN network architecture is proposed to construct the prediction model and improve the stability of model outputs significantly. The probabilistic forecasting is then used to actualize the model’s uncertainty evaluation by creating the confidence intervals using the Gaussian process. Consequently, experimental results reveal that the proposed boosting-SCN prediction model achieves superior forecasting accuracy than the single SCN model and other commonly used forecasting models. The probabilistic forecasting can efficiently obtain the uncertainties in power load data and provide support for system operation.

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.

An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation

As an irreplaceable power source, electricity is responsible for sustaining the national economy and social development, and the precondition for the power system’s stable operation is to perform an accurate short-term load forecast (STLF). However, with the increasing forms of social power consumption and the emergence of large-scale sustainable resources on the grid, which make STLF increasingly challenging as the power load exhibits greater stochasticity and instability. Therefore, a novel STLF system is developed in this paper, which incorporates data fuzzy granulation, a high-performance optimizer for integrating forecasting sequences, point and interval forecasts. Moreover, the performance tests of the optimization algorithm verify that our proposed optimizer can obtain more approximate solution sets to the real Pareto front and outshines the traditional optimization algorithm concerning convergence and diversity. Load data from three regions of Australia demonstrate that our developed system can remarkably contribute to the accuracy and stability of the STLF, and also quantify the volatility and uncertainty of the power load, which allows power workers to better capture the fluctuation interval of future loads and effectively enhance the flexibility of grid operation.

An Unsupervised Multi-Dimensional Representation Learning Model for Short-Term Electrical Load Forecasting

The intelligent electrical power system is a comprehensive symmetrical system that controls the power supply and power consumption. As a basis for intelligent power supply control, load demand forecasting in power system operation management has attracted considerable research attention in energy management. In this study, we proposed a novel unsupervised multi-dimensional feature learning forecasting model, named MultiDBN-T, based on a deep belief network and transformer encoder to accurately forecast short-term power load demand and implement power generation planning and scheduling. In the model, the first layer (pre-DBN), based on a deep belief network, was designed to perform unsupervised multi-feature extraction feature learning on the data, and strongly coupled features between multiple independent observable variables were obtained. Next, the encoder layer (D-TEncoder), based on multi-head self-attention, was used to learn the coupled features between various locations, times, or time periods in historical data. The feature embedding of the original multivariate data was performed after the hidden variable relationship was determined. Finally, short-term power load forecasting was conducted. Experimental comparison and analysis of various sequence learning algorithms revealed that the forecasting results of MultiDBN-T were the best, and its mean absolute percentage error and root mean square error were improved by more than 40% on average compared with other algorithms. The effectiveness and accuracy of the model were experimentally verified.