scholarly journals Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xue-Bo Jin ◽  
Hong-Xing Wang ◽  
Xiao-Yi Wang ◽  
Yu-Ting Bai ◽  
Ting-Li Su ◽  
...  
Keyword(s):  
Power System ◽  
Prediction Method ◽  
Bayesian Optimization ◽  
Load Prediction ◽  
Economic Operation ◽  
Power Load ◽  
Decomposition Structure ◽  
Optimization Parameters ◽  
Sustainable Power ◽  
Gated Recurrent Unit

The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.

Research on Short-Term Load Prediction Including the Photovoltaic System

2013 ◽  
Vol 860-863 ◽  
pp. 135-140
Author(s):  
Yang Lei ◽  
Shi Ping Zhou ◽  
Yong Jun Xia ◽  
Gang Hu ◽  
Xin Shu
Keyword(s):  
Power Generation ◽  
Power System ◽  
Energy Use ◽  
Negative Impact ◽  
Photovoltaic System ◽  
Support Vector ◽  
Load Prediction ◽  
System Load ◽  
Photovoltaic Power ◽  
Power Load

The global energy issues have become increasingly prominent in recent years, photovoltaic power generation as a renewable energy use pattern is widely used, but a large number of photovoltaic power generation to the grid is a big negative impact, it is necessary to predict the output power of the photovoltaic. Power system load forecasting is the reference and safeguard of the power system operation. This article analyzes the main point of the prediction of photovoltaic power system and power load system, then introduces the support vector machine (SVM) based on quantum particle swarm optimization (QPSO) to do the prediction. And then this paper proposes a generalized system load prediction system containing the photovoltaic power system.

scholarly journals Application of Deep Learning in Power load Analysis

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Prediction Method ◽  
Training Model ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Weather Information ◽  
Power Load ◽  
Prediction Time ◽  
Model Training

Aiming at the problems of slow model training speed and poor prediction effect of traditional power load prediction algorithm, a parallel load prediction method based on deep learning is proposed. The method is based on the MapReduce parallel calculating framework, and the deep belief network model, which is used to parallel training the sample data with the historical load and the weather information, and the model of the training model to predict the load value. The experimental results show that the average root-mean-square error between the predicted power load value and the actual value of the prediction method in this paper is 2.86%. The prediction accuracy is higher than the traditional method, and the training and prediction time are effectively reduced, which can adapt to the prediction requirements of large-scale power data.

scholarly journals A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 163
Author(s):  
Yaru Li ◽  
Yulai Zhang ◽  
Yongping Cai
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Recurrent Neural Networks ◽  
Critical Role ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Power Load ◽  
Gradient Calculation ◽  
Truncated Newton ◽  
Selection Of

The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experiences. In recent years, multiple algorithms based on Bayesian optimization (BO) are developed to determine the optimal values of the hyper-parameters. In most of these methods, gradients are required to be calculated. In this work, the particle swarm optimization (PSO) is used under the BO framework to develop a new method for hyper-parameter optimization. The proposed algorithm (BO-PSO) is free of gradient calculation and the particles can be optimized in parallel naturally. So the computational complexity can be effectively reduced which means better hyper-parameters can be obtained under the same amount of calculation. Experiments are done on real world power load data,where the proposed method outperforms the existing state-of-the-art algorithms,BO with limit-BFGS-bound (BO-L-BFGS-B) and BO with truncated-newton (BO-TNC),in terms of the prediction accuracy. The errors of the prediction result in different models show that BO-PSO is an effective hyper-parameter optimization method.

Research of power load prediction based on boost clustering

2021 ◽  
Author(s):  
Junde Chen ◽  
Defu Zhang ◽  
YA Nanehkaran
Keyword(s):  
Load Prediction ◽  
Power Load
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