Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show that the accuracy of our prediction model and early warning model is better than that of the baseline and can reach the standard of application in practice; this model can also be used for early warning of local sudden large loads and identification of enterprise power demand. Therefore, the validity of the method proposed in this paper is verified using the historical dataset of industrial parks, and relevant technical products and business models are formed to provide value-added services to users by combining existing practical cases for the specific scenario of industrial parks.

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The Forecasting Method Of Central Air Conditioning Load: A Brief Review

Recent Advances in Computer Science and Communications ◽

10.2174/2666255814666210201102854 ◽

2021 ◽

Vol 14 ◽

Author(s):

Mengxiang Zhuang ◽

Qixin Zhu

Keyword(s):

Air Conditioning ◽

Load Forecasting ◽

Current Status ◽

Impact Factors ◽

Future Research ◽

Load Prediction ◽

Advantages And Disadvantages ◽

Operating Load ◽

Forecasting Method ◽

The Impact

Objective: In order to better understand the research results of AC load prediction and carry out new research, the Air Conditioning (AC) load forecasting method plays an important role in the energy consumption of AC. Method: This paper summarizes the methods of building AC load prediction, mainly from the impact factors of AC operating load and the methods of AC system operating load forecasting to introduce the current status of load prediction. This paper describes some studies on load influencing factors, compares the advantages and disadvantages of modeling methods for AC operation load prediction and points out the research direction of AC load forecasting. Results: The current research methods are summarized and analyzed. Traditional forecasting methods are no longer applicable to air conditioning systems. From the current research, combinatorial prediction has become a hot research object. This method combines two or more methods to reduce the prediction error and shorten the prediction time. Conclusion: This paper points out some shortcomings of the present research and the future research suggestions are given in the three aspects of sharing AC operation data, selecting the key factors of AC, and exploring the new methods.

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Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

Sustainability ◽

10.3390/su131910526 ◽

2021 ◽

Vol 13 (19) ◽

pp. 10526

Author(s):

Jiajie Tang ◽

Jie Zhao ◽

Hongliang Zou ◽

Gaoyuan Ma ◽

Jun Wu ◽

...

Keyword(s):

Power System ◽

Prediction Accuracy ◽

Short Term Memory ◽

Time Series Prediction ◽

Load Forecasting ◽

Prediction Algorithm ◽

Load Prediction ◽

Forecasting Method ◽

Lstm Network ◽

Important Basis

The effective prediction of bus load can provide an important basis for power system dispatching and planning and energy consumption to promote environmental sustainable development. A bus load forecasting method based on variational modal decomposition (VMD) and bidirectional long short-term memory (Bi-LSTM) network was proposed in this article. Firstly, the bus load series was decomposed into a group of relatively stable subsequence components by VMD to reduce the interaction between different trend information. Then, a time series prediction model based on Bi-LSTM was constructed for each sub sequence, and Bayesian theory was used to optimize the sub sequence-related hyperparameters and judge whether the sequence uses Bi-LSTM to improve the prediction accuracy of a single model. Finally, the bus load prediction value was obtained by superimposing the prediction results of each subsequence. The example results show that compared with the traditional prediction algorithm, the proposed method can better track the change trend of bus load, and has higher prediction accuracy and stability.

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Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

Scientific Programming ◽

10.1155/2021/3250732 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Kai Zhang ◽

Wei Guo ◽

Jian Feng ◽

Mei Liu

Keyword(s):

Cloud Computing ◽

Deep Learning ◽

Clustering Algorithm ◽

Load Forecasting ◽

Load Prediction ◽

Computing Environment ◽

Density Peak ◽

Data Set ◽

Cloud Computing Environment ◽

Forecasting Method

For the problems of low accuracy and low efficiency of most load forecasting methods, a load forecasting method based on improved deep learning in cloud computing environment is proposed. Firstly, the preprocessed data set is divided into several data partitions with relatively balanced data volume through spatial grid, so as to better detect abnormal data. Then, the density peak clustering algorithm based on spark is used to detect abnormal data in each partition, and the local clusters and abnormal points are merged. The parallel processing of data is realized by using spark cluster computing platform. Finally, the deep belief network is used for load classification, and the classification results are input into the empirical mode decomposition-gating recurrent unit network model, and the load prediction results are obtained through learning. Based on the load data of a power grid, the experimental results demonstrate that the mean prediction error of the proposed method is basically controlled within 3% in the short term and 0.023 MW, 19.75%, and 2.76% in the long term, which are better than other comparison methods, and the parallel performance is good, which has a certain feasibility.

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Early Warning Evaluation of Tuberculosis and Meteorological Factors in Shanxi Province Based on Dynamic Bayesian Network

10.21203/rs.3.rs-259121/v1 ◽

2021 ◽

Author(s):

Meichen Li ◽

Zhuang Zhang ◽

Hao Ren ◽

Yueling Fan ◽

Weimei Song ◽

...

Keyword(s):

Bayesian Network ◽

Early Warning ◽

Meteorological Factors ◽

Recognition Performance ◽

Dynamic Bayesian Network ◽

Shanxi Province ◽

Monthly Precipitation ◽

Cross Correlation Analysis ◽

Warning Model ◽

Better Than

Abstract Background: Tuberculosis is a major global public health problem. However, it is still in the exploratory stage that the study of the meteorological factors related to the incidence of tuberculosis in Shanxi Province. Therefore, it is very urgent to establish an early warning system that easily operate of tuberculosis. Method: The epidemiological characteristics of tuberculosis in Shanxi Province were described, and the Dynamic Bayesian Network early warning model was established by time series cross-correlation analysis and Bayesian Network.Results: 1. The reported incidence of tuberculosis in Shanxi Province showed an overall downward trend from 2008 to 2017, showing a phenomenon of high in the middle and low at both ends each year, with certain seasonal characteristics. 2. Based on the results of cross-correlation analysis, it is reasonable to use dynamic Bayesian model fitting with meteorological factors lagging for 2 months; the monthly average temperature and monthly precipitation are positively correlated with the incidence of tuberculosis, but the monthly mean air pressure is negatively. 3. Comparison of classification and recognition performance of the three models shows that DBN has the highest classification accuracy in the two regions, which indicates that DBN is better than the other two models in reflecting the performance of minority classes, and better for the comprehensive classification of minority classes and majority classes. Conclusion: 1. Shanxi Province has tuberculosis clustering in time, space and time and space. Incidence peak is in spring and early summer. March is the highest month in the year. Seven meteorological factors such as monthly precipitation are the main factors affecting the incidence of tuberculosis in Shanxi Province. 2. The classification and recognition performance of the Dynamic Bayesian Network early warning model of tuberculosis-meteorological factors established in this study is significantly better than that of static Bayesian Network and support vector machine model, and can better predict the future.

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Establishment and Validation of Early-warning Model of Perioperative Pain for the Elders

Case Medical Research ◽

10.31525/ct1-nct04228705 ◽

2020 ◽

Author(s):

Keyword(s):

Early Warning ◽

Early Warning Model ◽

Perioperative Pain ◽

Warning Model

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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