Temperature Prediction Model Research Articles

CSACL: A Channel Spatial Attention Convolutional LSTM Model for Short-Term Sea Surface Temperature Prediction

CSACL: A Channel Spatial Attention Convolutional LSTM Model for Short-Term Sea Surface Temperature Prediction

An Empirical Approach to Solar Photovoltaic Cell Temperature Prediction

Abstract Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting, especially in commercial power plants. The present study focused on the development of a hybrid machine learning based predictive model for solar photovoltaic cell temperature prediction in solar photovoltaic arrays. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for these parameters and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed to mimic experimentally measured data. Feature selection and dimensionality reduction were performed to reduce the input variables and maintain relevant data in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the Mean Absolute Percentage Error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.08 % while an R2 value of 0.99 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed an average of 25.52 % daily energy difference between a simulation which considered solar cell temperature and that which ignored solar cell temperature.

Research on Greenhouse Environment Prediction Based on GCAKF-CNN-LSTM

Highlights A GCAKF-CNN-LSTM model is proposed for greenhouse temperature and humidity forecasting. The grey correlation analysis is used to select the most relevant variables. Kalman filter is applied for denoising to improve the data quality. The proposed model achieves higher forecasting accuracy with the lowest forecasting errors. Abstract. Accurate prediction of temperature and humidity in the greenhouse environment is helpful to regulate the environment and promote crop growth. Aiming at the characteristics of nonlinear and strong coupling in the greenhouse environment, this article proposes a hybrid greenhouse temperature and humidity prediction model based on preprocessing algorithms, Convolution Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Firstly, grey correlation analysis (GCA) is used to screen the data features and analyze the factors affecting the temperature and humidity in the greenhouse. Secondly, data is denoised by the Kalman filter (KF) to reduce the noise interference. Thirdly, the local connection and weight sharing features of the CNN are applied to obtain effective features from the series, and the long- and short-term dependence relationships of the data are learned by using the LSTM networks. Finally, the proposed model is validated on the greenhouse data. Experimental results demonstrated that, compared with Back Propagation(BP), Gated Recurrent Units (GRU), and LSTM, the RMSE of temperature prediction results was reduced by 31.5%, 21.6%, 14.4%, and the MAE reduced by 48.5%, 41.0%, and 32.3%, respectively. The RMSE of humidity prediction results decreased by 28.3%, 2.73%, and 0.63%, and the MAE decreased by 69.4%, 54.5%, and 10.8%, respectively. The proposed model can improve prediction accuracy and provide a decision basis for improving the timeliness of the greenhouse environmental control system. Keywords: Convolutional neural network, Greenhouse environment prediction, Kalman filter, Long short-term memory network.

R&D and Application of Smart Park Energy Control Platform Combined with MLM Technology

Abstract As the core of park intelligence, the smart park energy control platform realizes the efficient management and optimal use of park energy by integrating and applying IoT technology. This study focuses on the research development and application of a smart park energy management and control platform using IoT technology, firstly constructing the IoT smart park platform, designing the login module, the park information module, and the energy consumption analysis module, and describing the steps of the smart park energy collection algorithm, and then designing the model of the energy prediction and planning algorithms, i.e.In the construction of the LSTM heating temperature prediction model and the GRU network modeling. Testing and research can be done by using a smart park as an example. The empirical study shows that the construction of the IoT smart park platform can improve the accuracy of monitoring, and the difference between the actual collection results and the system monitoring results is no more than 0.03 kW. After optimizing the configuration, the park’s energy supply system can meet the electric heating and cooling loads with optimal efficiency. Through the comprehensive evaluation of the operation of the extensive energy information monitoring platform, the environmental pollutant emissions are reduced by about 20%, and the platform has a remarkable effect on energy saving and emission reduction.

Analysis of new energy vehicle battery temperature prediction by combining BP neural network

Abstract With the rapid development of the new energy industry, the safety research of battery technology has become a key topic. This paper focuses on the temperature prediction of new energy vehicle batteries, aiming to improve the safety and efficiency of batteries. Based on the new energy vehicle battery management system, the article constructs a new battery temperature prediction model, SOA-BP neural network, using BP neural network optimized by SOA algorithm. This model can accurately predict the battery temperature, and the effectiveness of its temperature control is verified through experiments. The results show that the SOA-BP neural network model outperforms the traditional BP, CNN, and RNN models in temperature prediction. Regarding evaluation indexes, the model’s root mean square error (RMSE), mean absolute error (MAE), and R2_Score are 0.953, 0.909, and 0.837, respectively. It is worth noting that the model can effectively regulate and control the battery temperatures at different temperatures, ensuring that the maximum temperature difference of each battery is maintained within 5°C. The model can also be used to predict the temperature of the batteries in different temperatures. This battery temperature prediction model not only provides an effective means for predicting and controlling the battery temperature of new energy vehicles, but also provides an essential reference for improving the vehicle’s performance and developing energy management strategies. This study offers a new solution for the safety and efficiency of new energy vehicle batteries.

Prediction of Transformer Oil Temperature Based on an Improved PSO Neural Network Algorithm

Abstract: In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved PSO neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain extent. Background: The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters. Objective: This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements. Methods: The paper utilizes an oil temperature prediction model based on an improved PSO neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm. Results: This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm. Conclusion: This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.

Numerical investigation of boiler waterwall corrosion by integrated tube temperature prediction and sulfur evolution model

High temperature corrosion has become one of the leading causes of boiler waterwall tube failures because of the wide application of air-staged combustion in coal-fired boilers. High temperature corrosion is strongly affected by the H2S concentration and wall tube temperature distributions in the furnace. Particularly, the corrosion rate increases exponentially with the increase of tube temperature. However, by far most of the numerical studies on high temperature corrosion only considered the effects of H2S concentration while the critical effects of tube temperature were overlooked. Thus, this paper presents a comprehensive corrosion model that incorporates the key effects of both tube temperature and H2S concentration. Specifically, the tube temperature is predicted by a coupled combustion and hydrodynamic model that integrates the critical effects of both the boiler gas flow and steam flow on waterwall tube temperature. The H2S concentration is predicted by a sulfur evolution model describing the devolatilization process of coal sulfur species and their subsequent gaseous reactions. The tube temperature prediction model and H2S evolution model provide all the key parameters for accurate prediction of boiler waterwall corrosion. The corrosion model is applied to a 350 MW supercritical wall-fired boiler. The results show that the tube temperature varies significantly over the boiler waterwall, and hence, has significant impacts on the distribution of wall tube corrosion. Particularly, local high tube temperature zones could form on the waterwall areas of relatively low H2S concentration leading to substantially higher tube corrosion rates than the adjacent wall areas. The results demonstrate the importance of incorporating both the wall tube temperature and H2S concentration in the prediction of boiler waterwall corrosion.

Heat generation mechanisms during induction heating of cross-ply carbon fiber reinforced thermoplastic laminates

Induction welding is considered to be one of the most promising welding technologies for carbon fiber reinforced thermoplastic (CFRTP) composites. However, the uncertainty of heat generation mechanisms presents a challenge to regulating the temperature field uniformity. In this paper, a quantitative calculation method for the induction heating efficiency of various mechanisms was proposed for cross-ply CFRTP laminates. Combining electrical and thermal properties, the cross-ply CF/PEEK and CF/PPS induction heating experiments were designed to explore the quantity ratio of various heat generation mechanisms. The results show that the heat production at the junction is about 106-107 times that of fiber heating. The dielectric loss is dominant mechanism at the junction, and the proportions of heat generated by dielectric loss in CF/PEEK and CF/PPS laminates were 0.6688 and 0.8255, respectively. Furthermore, the temperature prediction model with various stacking sequences was established, showing a good agreement with the experimental temperatures.

Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN

To predict the coal spontaneous combustion temperature accurately and efficiently, this study proposes a model based on the Sparrow Search Algorithm (SSA) and Convolutional Neural Network (CNN). Firstly, the study analyzes the main gas reactions during the coal oxidation to pyrolysis process. Six gas indicators, namely O2, CO, C2H4, CO/ΔO2, C2H4/C2H6, and C2H6, are closely related to coal temperature. Subsequently, a prediction indicator system is established. Then, the excellent data mining capabilities of CNN are leveraged through deep learning, along with their unique advantages in local perception and weight sharing, and a CNN prediction model framework is constructed. Moreover, the comparison between the algorithm performances is executed and SSA is selected for optimization. Utilizing its exceptional global search capability and adaptability, SSA optimizes the seven hyper-parameters of the model, significantly enhancing prediction accuracy. In the final step, SSA-CNN is compared with five reference models on test samples. The SSA-CNN model showcases a maximum relative error of 0.155, outperforming other models. Moreover, the RMSE of this model yields 8.4500, which is also lower than other models. The results suggest that the combination of the selected gas indicators with the SSA-CNN model can accurately predict the spontaneous combustion temperature of coal.

Data center temperature prediction and management based on a Two-stage self-healing model

While providing efficient and convenient cloud services, data center also brings great pressure to energy consumption and environment. The rise of server temperature not only increases the refrigeration cost, but also seriously affects the operation safety of the data center. Effective analysis and prediction of data center temperature is not only conducive to preventing server overheating and shutdown, but also crucial to data center task scheduling, resource allocation optimization and energy efficiency improvement of data center. Therefore, this article proposes a Two-stage Gated Recurrent Unit (GRU) temperature prediction algorithm with self-healing mechanism. The algorithm establishes a prediction model for the important parameters affecting temperature prediction - CPU utilization, and takes the output of the model as the input parameter of the server temperature prediction model, which fits the changes of each parameter more accurate. To avoid the decrease in prediction accuracy caused by new operating conditions that have not been learned before and changes in physical environmental factors during the operation of the model, a self-healing mechanism is proposed to ensure the prediction accuracy of the model. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. It reduces the prediction error (RSME) to 0.280, and the average prediction temperature difference is only 0.675, which is 10 % higher than the single stage prediction accuracy. The use of Two-stage prediction methods in other machine learning methods can also improve prediction accuracy. Based on the prediction model, this paper proposes a task scheduling algorithm that minimizes temperature difference. The algorithm can make the temperature between servers more balanced after task allocation, effectively reducing the number of servers running at high and low temperatures in the data center, avoiding refrigeration waste, and achieving energy conservation in the data center.

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes.

Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three machine learning (ML) models for transition temperature (T1/2) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated T1/2 values with a Pearson's r of 0.82. In contrast, DFA-predicted T1/2 values have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training.

A comparative analysis of deep learning models for soil temperature prediction in cold climates

A comparative analysis of deep learning models for soil temperature prediction in cold climates

A novel global average temperature prediction model——based on GM-ARIMA combination model

A novel global average temperature prediction model——based on GM-ARIMA combination model

A method for predicting the ambient temperature distribution of high-temperature tunnels and influencing factors analyze

In high-temperature tunnels, it is difficult to predict the temperature distribution, and the effectiveness of comprehensive cooling measures is not yet clear. This study proposed a highly accurate tunnel environmental temperature prediction model using FDM and FEM and considering the heat transferred from rock, construction machinery, and personnel. The effects of air volume, air temperature, and insulating layer on tunnel ambient temperature were investigated. The following are the key findings: (1) The distribution of the environment temperature and the lining surface temperature of the two tunnels align with the ground temperature distribution. (2) The prediction model using FDM + FNM closely matches the actual monitoring data, with a maximum absolute error of only 2.52 °C. (3) Increasing air volume or decreasing the temperature of the wind can reduce the temperature of the environment, which has no effect on the temperature distribution of tunnels. (4) The thermal insulating layer impedes ground temperature heat transfer and alters the ambient temperature distribution. The distribution of ambient temperature tends to align as the thickness of the thermal insulating layer grows, while cooling efficiency drops when the thickness is greater than 6 cm.

신재생 에너지 최적 활용을 위한 축열조 온도 예측 모델 연구

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

A knowledge transfer online stochastic configuration network-based prediction model for furnace temperature in a municipal solid waste incineration process

To alleviate the concept drift of an offline prediction model for the furnace temperature in a municipal solid waste incineration (MSWI) process caused by changes in working conditions, a prediction method for the furnace temperature based on a knowledge transfer online stochastic configuration network is proposed in this work. The proposed method includes offline learning and online learning. First, a stochastic configuration network is utilized to construct the offline furnace temperature prediction model, and a knowledge transfer method is employed to update the model under new operating conditions in the offline learning stage. Then, the updated model is used as the initial state of online modeling. Second, the recursive solution of the model output weights is presented to adapt to the dynamic change in incineration conditions, and a direction forgetting mechanism is utilized to enhance the result of the prediction model for the furnace temperature under nonpersistence of excitation in the online stage. Finally, to further verify the proposed online modeling method, the real historical data of an MSWI plant are utilized to finish the comparative experiments. The experimental results with the other methods show that the proposed prediction method for the furnace temperature presents a smaller error. Hence, the proposed method can reduce the influence of working conditions on the accuracy of furnace temperature prediction models.

Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes

The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the single model and the hybrid model) have their limitations in dealing with nonlinear and complex temperature data, making it challenging to achieve high-precision prediction results. In order to tackle this issue, this paper introduces a novel two-phase deep learning network for predicting the temperature of wind turbine gearboxes. In the first phase, a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory (BiLSTM) network are separately trained using the same dataset. The two pre-trained networks are combined and fine-tuned to form the 1DCNN-BiLSTM model for the accurate prediction of gearbox temperatures in the second phase. The proposed model was trained and validated by measured datasets from gearboxes from an existing wind farm. The effectiveness of the model presented was showcased through a comparative analysis with five traditional models, and the result has clearly shown that the proposed model has a great improvement in its prediction accuracy.

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An invitro study.

This study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real-time temperature prediction models. A combination of invitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six-axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models. By incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set. The proof-of-concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot-assisted implant surgery.

Prediction model of goaf coal temperature based on PSO-GRU deep neural network

The accurate determination of coal temperature in hidden space such as goaf has always been a worldwide problem that needs to be solved. The research of coal temperature prediction model has important practical significance for the accurate detection of loose coal temperature. Based on the coal natural ignition experiment and coal spontaneous combustion gas characterization index as the initial data set GRU neurons are used to mine the nonlinear relationship between the index gas and temperature, and the GRU model parameters are optimized by PSO to obtain the predicted value of coal body temperature. The results show that the predicted MAE value of the PSO-GRU model is 1.37 °C, 6.51 °C, 11.40 °C, 15.90 °C, 20.20 °C lower than that of the PSO-SVM, PSO-BP, BP, RF and SVM prediction models respectively. The RMSE value decreased by 0.45 °C, 4.44 °C, 10.33 °C, 15.71 °C and 24.24 °C respectively. The judgment coefficient R2 on the test set and the training set of the PSO-GRU model is 0.99, and the generalization, prediction accuracy and robustness are all good. The experimental results show that the inversion temperature error of the model is within the range of 3.87 %, the maximum temperature difference is 5.69 °C, and the average temperature difference is 2.83 °C, which can meet the accuracy requirements of the field temperature measurement.

A one-dimensional transient temperature prediction model for Chinese assembled solar greenhouses

The prediction of the thermal environment of greenhouses has always been an essential component of greenhouse development. In this paper, a one-dimensional transient temperature prediction model is constructed based on the principle of energy balance for Chinese assembled solar greenhouses (CASG). The daily temperature trends for each surface in the greenhouse were obtained by solving the model using MATLAB. The performance of the model was assessed by comparing the measured and predicted values. The mean absolute errors for the temperature of air, back roof, front roof, heat collector plate, north wall, ground and water storage tank were computed to be 1.4 °C, 1.89 °C, 1.98 °C, 2.07 °C, 2.26 °C, 1.44 °C and 0.68 °C, respectively. The results demonstrate a high level of concurrence between the simulated and measured values, indicating that the model is proficient in predicting the thermal environment of CASG. Based on this, predictions were made regarding the internal environment of CASG on both sunny and cloudy days, which contributes to a comprehensive understanding of the thermal environment. The research provides theoretical support for regulating environmental conditions of CASG.