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Multi-output discrete grey model tailored for electricity consumption forecast

APPLIED MATHEMATICAL MODELLING 2025年 139卷

作者： Zhang, Xu Dang, Yaoguo Ding, Song Wang, Haowei Ding, Feipeng Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Jiangxi Univ Finance & Econ Sch Stat & Data Sci Nanchang 330013 Peoples R China

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the bayesian optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

关键词： Grey forecasting Spatial correlation and spatial heterogeneity bayesian optimization algorithm Regularization Electricity consumption forecasting

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A Novel Deep Learning Based Classification of Building Roof Types Using Point Cloud Data

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年第3期53卷 815-826页

作者： Yildirim, Merve Karsli, Fevzi Karadeniz Tech Univ Engn Fac Dept Geomat Engn Trabzon Turkiye

The classification of various building roof types within point clouds has become a significant research topic, as it constitutes a crucial step for numerous applications, including the automatic detection and extraction of buildings, urban area mapping, and energy potential analysis. This study aims to develop a novel deep learning architecture that uses point clouds as input data to classify different types of building roofs. For this purpose, the proposed architecture, whose hyperparameters are optimized using the bayesian optimization algorithm, is trained and tested on the RoofN3D dataset. This architecture classified three different types of roof on the test data with an overall classification accuracy of 95%. In addition, the classification performance of the proposed architecture is compared with the well-known PointNet architecture. The outputs indicate that the classification performance of the two architectures is almost identical, with only a small difference due to the fact that the original design of the proposed architecture has fewer layers and thus a shorter training time. Therefore, the proposed architecture is an important method for automatically classifying different building roof types from point cloud data.

关键词： Point cloud Roof type classification Deep learning bayesian optimization algorithm

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Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 233卷

作者： Chen, Guangxu Tian, Hailong Xiao, Ting Xu, Tianfu Lei, Hongwu Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China Jilin Univ Jilin Prov Key Lab Water Resources & Environm Changchun 130021 Peoples R China Univ Utah Dept Civil & Environm Engn Salt Lake City UT USA Univ Utah Energy & Geosci Inst Salt Lake City UT 84108 USA Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun 130021 Peoples R China

An accurate prediction of oil production is critical for the oilfield development, and many deep learning models have been widely employed for this purpose. However, those methods show insufficiencies in extracting complex features from multivariable time series datasets, which leaves the prediction of oil production still challenging. In this study, a novel CNN-GRU model combining Convolutional Neural Networks (CNN) and Gate Recurrent Unit (GRU) neural network was proposed to accurately predict oil production for Enhanced Oil Recovery (EOR) performance. The CNN layer can extract the features from variables affecting oil production, and the GRU layer models temporal information using the transmitted features for prediction. The bayesian optimization algorithm (BO) was employed to design the optimal hyper-parameters of CNN-GRU. For evaluation purpose, two case studies were carried out with the production data from a CO2-EOR project and a waterflooding project. The prediction performance of the proposed approach was compared with typical deep learning methods and a hybrid (statistical and machine learning) method. The results of experiments and comparisons indicate that the proposed CNN-GRU model outperforms other prediction approaches. The CNN-GRU model provides future oil production of wells, enabling engineers to make informed decisions in development plan of reservoirs.

关键词： Oil production forecasting Deep learning Convolutional neural network Gate recurrent unit EOR bayesian optimization algorithm

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Enhancement of LSTM models based on data pre-processing and optimization of bayesian hyperparameters for day-ahead photovoltaic generation prediction

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 116卷

作者： Casanova, R. Herrera Conde, A. Autonomous Univ Nuevo Leon San Nicolas De Los Garza Mexico

In this paper, a forecasting method for day -ahead photovoltaic (PV) generation using long shortterm memory (LSTM) neural network is presented. To improve the quality of the predictions, the most relevant hyperparameters of the proposed model are adjusted using a bayesian optimization algorithm. The study uses a database obtained from a grid -connected solar plant containing historical data of the PV power generated, solar irradiance, ambient temperature, PV panel temperature and wind speed. First, a preprocessing algorithm is applied to enhance the quality of the data and the accuracy of the forecasts. Subsequently, the proposed model is used to predict the power generated at the facility for the following day. The obtained results are compared with other types of models used in the related literature: a gated recurrent unit (GRU) neural network and a multilayer perceptron (MLP) neural network. The tests are performed on days with different meteorological behavior, and it is observed that the proposed model outperforms the comparison models in all cases analyzed in terms of accuracy and quality of predictions.

关键词： Photovoltaic power forecasting Long short-term memory bayesian optimization algorithm Data preprocessing Photovoltaic plant

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Classification forecasting research of rock burst intensity based on the BO-XGBoost-Cloud model

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EARTH SCIENCE INFORMATICS 2025年第1期18卷 1-14页

作者： Yuan, Haiping Ji, Shuaijie Li, Hengzhe Zhu, Chuanqi Zou, Yangyao Ni, Bin Gu, Ziang Anhui Univ Sci & Technol State Key Lab Min Induced Response & Disaster Prev Huainan 232001 Peoples R China Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China

The rock burst belongs to a serious engineering geological disaster among the underground and deep resource exploitation engineering. The prediction of rock burst intensity has become an urgent problem to be addressed in underground engineering. In order to effectively predict the rock burst intensity grades, the four indicators were comprehensively selected in this paper to establish a rock burst evaluation indicator system. More specifically, the bayesian optimization algorithm was employed to optimize the XGBoost machine learning algorithm to determine the weights of various evaluation indicators. Afterwards, the cloud model theory was introduced into the grade prediction study of rock burst intensity, as well as ultimately establishing a comprehensive evaluation model of rock burst intensity based on the BO-XGBoost-Cloud model. Consequently, the feasibility and validity of the model were examined with the assistance of on-site measured data and rock burst discrimination results among the relevant literature, which indicated that: the BO- XGBoost-Cloud model exhibited the highest accuracy in predicting rock burst intensity, significantly surpassing the extension evaluation method and the entropy-cloud model. Therefore, the BO-XGBoost-Could model was the optimal model for predicting rock burst intensity, which provided a creative forecasting method for the classification prediction of rock burst intensity in underground engineering.

关键词： Rock burst Underground engineering XGBoost bayesian optimization algorithm Cloud model

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Image-based automatic multiple-defect detection of urban utility tunnel using UUTNet

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ADVANCES IN STRUCTURAL ENGINEERING 2024年第7期27卷 1170-1188页

作者： Sun, Weihao Hou, Shitong Wu, Gang Feng, Dongming Fan, Jianhua Southeast Univ Sch Civil Engn Sipailou 2 Nanjing 210096 Peoples R China Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Constr Nanjing Peoples R China

In-service urban utility tunnels (UUT) suffer from cracks, corrosion, and leakage defects, which rises the chance of major accidents. However, prevailing detection methods for UUT remain reliant on manual inspection and subjective judgment, or traditional image processing technologies, such methods may not be able to obtain accurate defect information. This study proposes a novel and effective network called UUTNet based on the constructed UUT dataset for defects detection. Considering that the UUT defects has a certain distribution correlation, the attention module is introduced to the Pyramid Scene Parsing Network to capture the relation. By adding the hybrid dilated convolution after the feature extraction layer, the receptive field is expanded to further extract global and local features. The performance of UUTNet was evaluated based on the metrics MIoU, F1-score, Accuracy, and robustness. Comparative experiments were conducted, and the results showed the UUTNet achieved the best detection performance, achieving 0.7615 MIoU, 0.9806 Accuracy and 0.8012 F1-score. The MIoU was further improved to 0.7847 by utilizing the bayesian optimization. Three extreme inspection scenes, including uneven illumination, high brightness, and obstacle interference, were applied to validate model robustness. The proposed method offers robust technical assistance for detecting defects in the UUT and precisely assessing the distribution and extent of these defects.

关键词： Urban utility tunnel safety defect detection deep learning intelligent inspection bayesian optimization algorithm

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A new prediction model based on deep learning for pig house environment

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SCIENTIFIC REPORTS 2024年第1期14卷 1-16页

作者： Wu, Zhidong Xu, Kaixiang Chen, Yanwei Liu, Yonglan Song, Wusheng Qiqihar Univ Sch Mech & Elect Engn Qiqihar 161006 Peoples R China Engn Technol Res Ctr Precis Mfg Equipment & Ind Pe Qiqihar 161006 Peoples R China Heilongjiang Acad Agr Sci Harbin 150000 Peoples R China

A prediction model of the pig house environment based on bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations. The SE block further learns the weights of the feature channels, highlights the important features and suppresses the unimportant ones, improving the feature discrimination ability. The extracted local features are fed into the GRU network to capture the long-term dependency in the sequence, and this information is used to predict future values. The indoor environmental parameters of the pig house are predicted. The prediction performance is evaluated through comparative experiments. The model outperforms other models (e.g., CNN-LSTM, CNN-BiLSTM and CNN-GRU) in predicting temperature, humidity, CO2 and NH3 concentrations. It has higher coefficient of determination (R2), lower mean absolute error (MSE), and mean absolute percentage error (MAPE), especially in the prediction of ammonia, which reaches R2 of 0. 9883, MSE of 0.03243, and MAPE of 0.01536. These data demonstrate the significant advantages of the BO-SE-CNN-GRU model in prediction accuracy and stability. This model provides decision support for environmental control of pig houses.

关键词： Pig house bayesian optimization algorithm Convolutional neural network Gated recurrent unit Squeeze and excitation Environmental prediction model

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A novel hybrid forecasting model with feature selection and deep learning for wind speed research

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JOURNAL OF FORECASTING 2024年第5期43卷 1682-1705页

作者： Chen, Xuejun Wang, Ying Zhang, Haitao Wang, Jianzhou Gansu Meteorol Serv Ctr Lanzhou 730020 Gansu Peoples R China Hainan Univ Int Business Sch Haikou 570228 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two-stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long-short term memory, which is optimized by the bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10-min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

关键词： bayesian optimization algorithm data preprocessing deep learning feature selection forecasting wind speed

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Hybrid model for daily runoff interval predictions based on bayesian inference

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HYDROLOGICAL SCIENCES JOURNAL 2023年第1期68卷 62-75页

作者： Xu, Hanbing Song, Songbai Li, Jun Guo, Tianli Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling 712100 Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling Peoples R China

The highly non-linear and nonstationary nature of runoff events in changing environments makes accurate and reliable runoff forecasting difficult. We propose a hybrid model by integrating an autoregressive (AR) model, bayesian inference, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, bayesian optimization, and support vector regression. Two bayesian inference methods (the No-U-Turn Sampler (NUTS) and variational inference) were used to calculate the parameters of the AR model to obtain a bayesian AR (BAR) model. Credible intervals were used to analyse the uncertainty of the parameters and model prediction results. The above model is applied to the daily runoff predictions of hydrological stations in the Yellow River basin of China. The results show that (1) the hybrid model can improve the prediction accuracy and (2) the NUTS algorithm-based model provides a narrower reliable interval and performs better in uncertainty analyses.

关键词： bayesian inference autoregressive model support vector regression bayesian optimization algorithm daily runoff prediction Yellow River basin

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Short-term load forecasting based on CNN-BiLSTM with bayesian optimization and attention mechanism

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第17期35卷

作者： Shi, Huifeng Miao, Kai Ren, Xiaochen North China Elect Power Univ Dept Math & Phys Baoding 071003 Peoples R China

Accurate short-term load forecasting can ensure the reliable power supply of the power system and the regular operation of the grid economy. This article proposes a hybrid model based on convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) with bayesian optimization (BO) and attention mechanism (AM) for short-term load forecasting. CNN is used to capture the significant features of input data. BiLSTM is adept in time series forecasting and AM can reduce the computational complexity of model. BO can help to tune the hyperparameters automatically. The input features of models are recent load, time slot, date type, and meteorological factors. In order to eliminate the seasonality, the data set is divided into four subsets according to four seasons. The performance of the proposed model is compared with other models by MAE, RMSE, MAPE, and R2 score. The forecasting results represent that the proposed model is most suitable for short-term load forecasting among the contrast models and the meteorological factors have an impact on forecasting accuracy.

关键词： attention mechanism bayesian optimization algorithm bidirectional long short-term memory network convolutional neural network short-term load forecasting

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