Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

scholarly journals Short-term load prediction of integrated energy system based on neural network

2021 ◽  
Vol 2087 (1) ◽  
pp. 012016
Author(s):  
Yao Wang ◽  
Xuxia Li ◽  
Yan Liang ◽  
Yingying Hu ◽  
Xiaoming Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Energy System ◽  
Load Prediction ◽  
Short Term ◽  
Nonlinear Characteristics ◽  
Integrated Energy System ◽  
Long Short Term Memory ◽  
Grey Relation

Abstract Considering the correlation and nonlinear characteristics of multiple types of loads in the integrated energy system, grey relation analysis (GRA) and long short term Memory (LSTM) neural network are selected to establish the short-term load prediction model of the integrated energy system. The model uses GRA method to analyze the coupling between multiple types of loads and the meteorological factors, and then obtains the load forecast results through the LSTM prediction model. Finally, a practical example is given to verify the validity of the model.

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

2021 ◽  
Vol 16 (4) ◽  
pp. 35-51
Author(s):  
Bingchun Liu ◽  
Xiaogang Yu ◽  
Qingshan Wang ◽  
Shijie Zhao ◽  
Lei Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Maximum Temperature ◽  
Optimal Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Concentration Prediction

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

scholarly journals Reactive Load Prediction Based on a Long Short-Term Memory Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 90969-90977
Author(s):  
Xu Zhang ◽  
Yixian Wang ◽  
Yuchuan Zheng ◽  
Ruiting Ding ◽  
Yunlong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 941
Author(s):  
Seongyoep Jeong ◽  
Inyoung Park ◽  
Hyun Soo Kim ◽  
Chul Han Song ◽  
Hong Kook Kim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Image Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Long Short Term Memory

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

scholarly journals Bridge Temperature Prediction Model Based on Long-Short Term Memory Neural Network

2021 ◽  
Vol 1966 (1) ◽  
pp. 012013
Author(s):  
Jingxiao Shu ◽  
Dongyue Zhao ◽  
Xuda Zheng ◽  
Yiwen Li ◽  
Yufeng Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory

scholarly journals A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

2020 ◽  
Vol 15 (5) ◽  
Author(s):  
Yonghong Tian ◽  
Qi Wu ◽  
Yue Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Supply And Demand ◽  
Original Data ◽  
Market Demand ◽  
Short Term ◽  
Term Memory ◽  
Demand Prediction ◽  
Long Short Term Memory

In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments.

scholarly journals Failure Mechanism and Long Short-Term Memory Neural Network Model for Landslide Risk Prediction

2021 ◽  
Vol 14 (1) ◽  
pp. 166
Author(s):  
Xuan Zhang ◽  
Chun Zhu ◽  
Manchao He ◽  
Menglong Dong ◽  
Guangcheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Discrete Element ◽  
Slope Instability ◽  
Monitoring Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters.

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