scholarly journals VMD-WSLSTM Load Prediction Model Based on Shapley Values

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 487
Author(s):  
Bilin Shao ◽  
Yichuan Yan ◽  
Huibin Zeng
Keyword(s):  
Prediction Model ◽  
Decomposition Algorithms ◽  
Load Prediction ◽  
Model Based ◽  
Proposed Model ◽  
Address Data ◽  
Shapley Values ◽  
Complex Models ◽  
Optimal Set ◽  
And Training

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system

Energy ◽  
2020 ◽  
Vol 199 ◽  
pp. 117454
Author(s):  
Jianjuan Yuan ◽  
Zhihua Zhou ◽  
Huajie Tang ◽  
Chendong Wang ◽  
Shilei Lu ◽  
...  
Keyword(s):  
Control System ◽  
Prediction Model ◽  
User Behavior ◽  
Load Prediction ◽  
Model Based ◽  
Heating Load

scholarly journals Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16726-16741 ◽  
Author(s):  
Jiancai Song ◽  
Guixiang Xue ◽  
Xuhua Pan ◽  
Yunpeng Ma ◽  
Han Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Heat Load ◽  
Load Prediction ◽  
Model Based

scholarly journals A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Guohui Li ◽  
Songling Zhang ◽  
Hong Yang
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Extreme Point ◽  
Prediction Models ◽  
Original Data ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Model Based ◽  
Mode Decomposition ◽  
Proposed Model

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

An Improved Photosynthesis Prediction Model Based on Artificial Neural Networks Intended for Cucumber Growth Control

2018 ◽  
Vol 34 (5) ◽  
pp. 769-787 ◽  
Author(s):  
Pingping Xin ◽  
Haihui Zhang ◽  
Jin Hu ◽  
Zhiyong Wang ◽  
Zhen Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Photosynthetic Rate ◽  
Prediction Models ◽  
Ann Model ◽  
Rate Prediction ◽  
Model Based ◽  
Proposed Model ◽  
Artificial Neural

Abstract. The existing photosynthetic rate prediction models consider only a single growing season. However, a photosynthetic rate prediction model intended for full growth of crops is needed. Therefore, a photosynthetic rate prediction model based on artificial neural networks (ANN), which establishes the prediction of the entire photosynthetic process, is presented in this article. The proposed model was developed using the multi-factor photosynthetic rate data obtained by experiments on cucumber seedlings and flowering stage. The ANN model was trained with the Levenberg-Marquardt (LM) training algorithm. In contrast to the single-phase photosynthetic rate prediction models, in the proposed model a fusion of parameters of all growing stages was applied, whereat all growing parameters were merged into one six-dimensional input signal (temperature, CO2 concentration, light intensity, relative humidity, chlorophyll content, and growth stage). Verification of model accuracy and performance has shown that merging of growing parameters has obvious advantage. Moreover, the proposed model satisfied the requirement in terms of training error. In addition, the determination correlation between measured and estimated values was 0.9517, thus, good correlation and estimation were achieved. Besides, the test average absolute error was 1.1454, which proves a high accuracy of the proposed model. Therefore, the proposed prediction model can provide the theoretical basis for the facilities light regulation and technical support. Keywords: Artificial neural networks, Cucumber, Full growth period, Photosynthetic rate, Prediction model.

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