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

Jeong, Seongyoep; Park, Inyoung; Kim, Hyun Soo; Song, Chul H.; Kim, Hong Kook

doi:10.3390/s21030941

Cited by 25 publications

(12 citation statements)

References 26 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of contemporary urban sculpture, sculpture presents a diversified and intersecting development model, and the sculpture art has a trend of "freehand sculpture," as well as the expression form of installation sculpture. The representative figure of "freehand sculpture" is Wu Weishan [8].…”

Section: Current Situation Ofmentioning

confidence: 99%

Dimensional Defect Detection Research on the Sculpture Surface Combining the Convolutional Neural Network and Gabor

Liu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Thematic sculpture creation is an important part of sculpture creation. In particular, the current thematic sculpture creation in China has broken the shackles of serving politics and has shown a variety of aspects such as theme selection, modelling method, material language, and creative purpose. Since the new century, Chinese thematic sculptors have made in-depth analysis and research on the diversity of creative objects, the infinite possibilities of creative expression, and the diversity of creative purposes, especially through the phenomenon of different thematic sculptures to explore their existing predicaments, and give them reflection. If the time is too long, it will reduce the production efficiency of the sculpture production line and increase the operating cost of the enterprise. The Gabor wavelet transform is used to obtain the texture features of the sculpture surface, and the generated high-dimensional features are subjected to random projection dimension reduction. Finally, the binarization algorithm can be used to detect the defect location quickly and accurately. The feature map is extracted by selecting a deep network. The neural network algorithm structure for the sculpture dataset is presented, and the activation function and regularization method are improved on this basis.

show abstract

Section: Current Situation Ofmentioning

confidence: 99%

Dimensional Defect Detection Research on the Sculpture Surface Combining the Convolutional Neural Network and Gabor

Liu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Air-temperature forecasting is one of the major issues in the meteorology and climatology domains, which explains why it is fairly well-researched. However, it concerns daily, monthly, or annual temperature time series measured with low and medium time resolution (long-and medium-term predictions) [24,25]. The time series of meteorological parameters for use in heating systems must concern much smaller time intervals (hours, minutes), and the methodology for forecasting such short-term data has not yet to be developed.…”

Section: Scopementioning

confidence: 99%

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

et al. 2021

View full text Add to dashboard Cite

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

show abstract

“…Forecasting air temperature is one of the main topics in the field of meteorology and climatology, which explains why it is fairly well researched. However, this applies to daily, monthly or annual time series of temperature measured with low and medium time resolution (long-and medium-term forecasts) [73][74][75][76][77]. Typically, the prediction of meteorological parameters is used at an early stage of the heat load estimation without going into its methodology [78].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Thermo-Modernization and Predictive Regulation on Heat Energy Consumption and Emission of Pollutants into the Atmosphere - The Case of Prefabricated Buildings

Piotrowska-Woroniak¹,

Cieśliński²,

Woroniak³

et al. 2022

Which Factors Contribute to Innovative Performance? A Case Study Applied to the Food and Beverage Industry

View full text Add to dashboard Cite

The paper presents an assessment of thermal energy consumption for heating in 10 buildings made in the OWT-67N prefabricated large-panel technology from 1983 to 1986. The work covers the years 2002-2020 in three periods: before and after thermal modernization and after the use of an innovative weather prediction heating system control in buildings. The analysis made it possible to assess the impact of carrying out a deep thermal modernization, and then installing a modern forecast regulation system in terms of reducing heat energy consumption for central heating purposes, as well as reducing greenhouse gas emissions, such as CO2, SOx, NOx, CO and www.videleaf.com benzo(a)pyrene, into the atmosphere. The implementation of deep thermal modernization in buildings allowed for savings of 19.8-35% of thermal energy consumption for heating. The use of additional regulation based on prediction saved from 4.8 to 23.5%, except for one building BU10, where there was an increase in final energy consumption by 2.1%. Replacing the weather regulation in heating stations with the forecast regulation additionally reduced the emission of pollutants by 11.1%, compared to the reduction of pollutants achieved as a result of the thermal modernization of buildings alone, amounting to an average of 29.7%.

show abstract

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

Cited by 25 publications

References 26 publications

Dimensional Defect Detection Research on the Sculpture Surface Combining the Convolutional Neural Network and Gabor

Dimensional Defect Detection Research on the Sculpture Surface Combining the Convolutional Neural Network and Gabor

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

The Impact of Thermo-Modernization and Predictive Regulation on Heat Energy Consumption and Emission of Pollutants into the Atmosphere - The Case of Prefabricated Buildings

Contact Info

Product

Resources

About