Operational Data Research Articles

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

A study on multiple types of yard cranes rescheduling in ports considering carbon emission

ABSTRACT This paper investigates the rescheduling problem of multiple oil-driven and electric-driven yard cranes across multiple container yards in semi-automated container terminals under random faults. Efficient fault diagnosis and handling mechanisms are provided to mitigate the effects of faults on the performance of yard operations. At the same time, a rescheduling model is proposed, aiming to minimize weighted carbon emissions and completion time. By analyzing the features of the problem, a tailored adaptive large neighborhood search (ALNS) algorithm is designed to solve the model. The effectiveness of the ALNS is verified through problem instances generated from the operational data of the port of Dalian in China. The factors considered in this paper are relevant to actual port operations, thus offering reliable solutions for scheduling and greening of ports.

Flexible Ureteroscopy versus Retroperitoneal Laparoscopic Ureterolithotomy in the Management of upper Ureteric Stones Measuring from One to Two Centimeters: Randomized Prospective Study

Abstract Objective This prospective study aims to compare the clinical efficacy and safety of flexible ureteroscopy versus laparoscopic ureterolithotomy in the management of upper ureteric stones measuring from one to two centimeters. Methods his randomized-prospective study was conducted on 64 patients admitted from Urology unit, Ain Shams University in the period from October 2020 till September 2022; only 56 patients were analyzed statistically and 8 patients were excluded due to shortage in post- operative data and follow up. Patients were divided into two groups Group A consists of 31 patients and Group B consists of 25 patients with matched age, gender and body mass index (BMI). Results In our study, The VAS score at 6 hours post-operative and day 1 post-operative was found to be 5.32 and 2.71 in FURS group and was found to be 7.28 and 3.28 in RPLU group respectively showing significant difference with (p- value <0.001 and 0.016 respectively) between the two groups with more satisfaction in FURS group mostly due to the absence of trocars incisions. The complications were classified according to modified Clavien classification system. No major complications such as septic shock or death were reported in either treatment groups. As regards the recovery to work in postoperative and follow up periods in the present study, there was a significant difference between both groups with (P-value <0.001), as we found that it takes about 4.56 ± 0.82 days in RPLU group and 3.06 ± 1.91 days in FURS group to return daily routine activities. As regards the stone free rate in follow up periods in the present study, there was a significant difference between both groups with (P-value 0,02), as we found that it was 100% and 80,6% in RPLU and FURS group respectively. Our success rate in RPLU group was found to be 100% in comparison to Irfan Nazir Mir et al which was 93.4% (28/30) with two patients (2/30) converted to open ureterolithotomy, either due to technical snag or due to difficulty in localizing the ureter laparoscopically. Conclusion Both F-URS and RTLU were safe and effective surgical procedures for treatment of large proximal ureteral stones. However, RTLU had a higher stone-free rate with comparable operating time and complication rate as compared to F-URS. Also as regards to postoperative pain, shorter hospital stay and faster return to daily activities, both have almost similar results.

Voltage control method for multi-energy system based on the coupling of renewable energy hydrogen production and storage

The power system is gradually becoming an AC/DC hybrid MES (multi-energy system) with hydrogen energy. Due to the uncertainty of reactive power demand of load in MES and the multi-time scale characteristics of different energy flow transmission. It will lead to difficulties in reactive power coordination within the entire MES. Secondly, in response to this problem, this paper first establishes a MES cluster hybrid power flow equation formed by AC/DC and hydrogen transmission channels. The reactive power voltage fluctuation characteristics of AC/DC systems under MES cluster interconnection channel energy exchange, system energy conversion and renewable energy output fluctuation are analysed. Then according to the MES cluster hybrid power flow calculation. The main factors affecting the voltage reactive power of the AC system are studied, and the reactive power model based on electrothermal conversion, hydrogen storage and multi-energy transmission channels is established. Finally, proposed an algorithm for generating the safety and stability boundary of pipe network pressure. It’s used to construct the safe and stable boundary of the system pipe network. based on the actual operational data of multi-energy power system. The simulation model of reactive power dynamic optimization control for multi-energy interconnected systems is established

Exergy analysis of a gas turbine cycle power plant: a case study of power plant in Egypt

AbstractThis research presents an exergy analysis of a gas turbine power plant situated in Assiut, Egypt, operating under high-temperature conditions. The aim of the study is to assess the performance of the simple gas turbine cycle and identify the sources of thermodynamic inefficiencies using the second law of thermodynamics as a basis for analysis. To accomplish this, a model was developed in EES software utilizing real operational data obtained from the plant's control system. The investigation focused on the impact of varying ambient temperature on the exergy efficiency, exergy destruction, and net power output of the cycle. The results revealed that the combustion chamber accounted for the highest exergy destruction, amounting to 85.22%. This was followed by the compressor at 8.42% and the turbine at 6.36%. The overall energy and exergy efficiencies of the system were determined to be 28.8% and 27.17%, respectively. Furthermore, the study examined the effects of increasing ambient temperature from 0 to 45°C on the system's performance. It was observed that as the temperature rose, the overall exergy efficiency decreased from 27.91 to 26.63%. Simultaneously, the total exergy destruction increased from 126,407 to 138,135 kW. Additionally, the net power output exhibited a decline from 88,084 to 84,051 kW across the same ambient temperature range. These findings highlight the significant influence of ambient temperature on the thermodynamic performance of gas turbine power plants. As temperature rises, a greater amount of exergy is lost, resulting in reduced efficiency and diminished net power output. Therefore, optimizing the design of the combustion chamber is crucial for mitigating the adverse effects of hot weather conditions. The insights obtained from this study can be utilized to enhance the design and operation of gas turbine plants operating in hot climates.

Data-Driven Management: The Impact of Big Data Analytics on Organizational Performance

Inevitably, in such a fast-moving landscape of big data analytics lies the transformative opportunity for any organization to unlock new avenues of growth, operational efficiency, and strategic decision-making. This paper contributes a comprehensive methodology that will help businesses take advantage of big data analytics to secure a continued competitive advantage. At the core of this methodology is to have a robust data governance framework that will establish the security, integrity, and accessibility of the enterprise data assets by specifying relevant policies, processes, and technologies. This, in turn, within such a framework, allows state-of-the-art AI-driven anomaly detection mechanisms for encryption and access control to implement protection measures around sensitive information while enabling secure yet efficient data utilization. The methodology also continues its approach of putting in place an integrated data ecosystem that brings together different pockets or sources of data, such as real-time operation data, customer interaction, and unstructured information. A critical component of this methodology is putting in place the advanced predictive analytics capabilities that could be run based on tapping into the power of machine learning algorithms; by doing so, the organization will be predicting market trends, customer behavior, and risks highly accurately. This will be a very proactive way of making decisions that would let the business efficiently use the available resources, innovate products and services ahead of time, and create a distinctly competitive advantage. The transformative ability of this methodology for big data analytics opened new channels toward growth and innovation and firmly established the organization as an industry leader with long-lasting competitive advantage. The place of data-driven insight, data culture, and responsible data practice has been the key to success in this organization.

Efficiency of eSource Direct Data Capture in Investigator-Initiated Clinical Trials in Oncology.

Clinical trials have become larger and more complex. Thus, eSource should be used to enhance efficiency. This study aimed to evaluate the impact of the multisite implementation of eSource direct data capture (DDC), which we define as eCRFs for direct data entry in this study, on efficiency by analyzing data from a single investigator-initiated clinical trial in oncology. Operational data associated with the targeted study conducted in Japan was used to analyze time from data occurrence to data entry and data finalization, and number of visits to the site and time spent at the site by clinical research associates (CRAs). Additionally, simulations were performed on the change in hours at the clinical sites during the implementation of eSource DDC. No difference in time from data occurrence to data entry was observed between the DDC and the transcribed data fields. However, the DDC fields could be finalized 4days earlier than the non-DDC fields. Additionally, although no difference was observed in the number of visits for source data verification (SDV) by CRAs, a comparison among sites that introduced eSource DDC and those that did not showed that the time spent at the site for SDV was reduced. Furthermore, the simulation results indicated that even a small amount of data to be collected or a small percentage of DDC-capable items may lead to greater efficiency when the number of subjects per site is significant. The implementation of eSource DDC may enhance efficiency depending on the study framework and type and number of items to be collected.

Development of a DNN model using calibrated simulation for the optimal control of HVAC systems

Acquiring actual data for developing data-based prediction models for the optimization of control setpoints (hereinafter referred to as optimal control (OC)) in heating, ventilation, and air conditioning (HVAC) systems is challenging, especially when changing control setpoints while considering all possible boundary conditions. Thus, considering the scalability of OC, it is a promising solution to generate sufficient learning data according to the control settings using a highly accurate simulation model, build a source model, and expand the learning model based on the actual data of the target system. This is the first study to improve the scalability of OC aiming to develop a deep neural network (DNN) model to predict the power consumption of a heat source system. First, this study will construct a calibrated heat source system simulation model and then use the generated learning data to train a DNN model. The actual operation conditions of the target building and heating system located in Vietnam were analyzed using operation data. A physics-based simulation model of the target heat source system was developed and calibrated using actual operation data to improve accuracy. By using the calibrated simulation, sufficient learning data was generated for all control settings allowing for the development of a learning model based on DNN that predicted the power consumption of the heat source system. In addition, a highly accurate prediction model through hyperparameter optimization was secured.

Fault root cause analysis using degree of change and mean variable threshold limit in non-linear dynamic distillation column

The chemical industrial processes are both dynamic and complex. The presence of instability and danger in the production process poses significant challenges to safety management. Early and accurate fault diagnosis is essential to ensure a sustainable and stable chemical process performance. Most studies in literature do not provide detailed information about diagnosing the root cause of faults in distillation columns using contribution plots. Using principal component analysis (PCA) in conventional contribution plots leads to a large number of contributing variables, making it difficult to identify the root cause of a specific fault. In this study, the authors suggest employing the Degree of Change of Contribution Values (DCC) and Mean Variable Threshold Limit (LMVT) techniques alongside the Wavelet Transform-Principal Component Analysis (WT-PCA) method to decrease the quantity of fault identification variables in a non-linear system (distillation column). The Aspen Plus software is utilized to perform a dynamic simulation of a distillation column, generating data for both normal and faulty operations. Two distinct types of faults are considered, which include loss of feed flow and feed temperature changes. This study aims to analyze the results obtained from the Hotelling T2 and SPE contribution plots by calculating the average difference between the normal and faulty conditions using the Degree of Change in Contribution Values. This procedure is performed to determine the extent of deviation from the standard state. Subsequently, the variables that have surpassed the threshold limit for means variables (LMVT) are designated as the variables that serve as the primary underlying cause of the fault. The technique was employed in a case study involving a nonlinear distillation column, and the results indicate that only one "dominant" variable intersects the LMVT by Hotelling T2, which represents the true underlying cause of the corresponding fault.

Long-Term Performance Evaluation and Fouling Characterization of a Full-Scale Brackish Water Reverse Osmosis Desalination Plant

Water scarcity in Tunisia’s semi-arid regions necessitates advanced brackish water desalination solutions. This study evaluates the long-term performance and fouling characteristics of the largest brackish water reverse osmosis desalination plant in southern Tunisia over a period of 5026 days. The plant employs two-stage spiral-wound membrane elements to treat groundwater with a salinity of 3.2 g L−1. The pre-treatment process includes oxidation, sand filtration, and cartridge filtration, along with polyphosphonate antiscalant dosing. Membrane performance was assessed through the analysis of operational data, standardization of permeate flow (Qps) and salt passage (SPs), and the calculation of water (A), solute (B), and ionic (Bj) permeability coefficients. Over the operational period, there was an increase in operating pressure, pressure drop, and permeate conductivity, accompanied by a gradual increase in SPs as well as in the solute B and ionic Bj permeability coefficients. The average B increased by 82%, reflecting a decrease in solute rejection over time. Additionally, the ionic permeability coefficients for both SO42− and Cl− ions increased, with Cl− showing an 88% increase and SO42− showing an 87% increase. The produced water’s salinity increased by 67%, indicating a significant loss of membrane performance. To identify the cause of these problems, membrane characterization was analyzed using visual inspection, X-ray fluorescence (XRF), and Fourier transform infrared spectroscopy (FTIR). The characterization revealed the complex nature of the foulants, with a predominant presence of calcium sulfate, along with minor quantities of calcite, dolomite, and silica. The extent of CaSO4 deposition suggests poor antiscaling efficiency, highlighting the critical importance of selecting an effective antiscalant to mitigate membrane fouling.

DEVELOPMENT OF A WEB-BASED APPLICATION FOR MANAGING ASSESSMENT AND INTERVIEW ACTIVITIES AT PT. GAIA SOLUTIONS

Information systems have significantly evolved in tandem with advancements in information technology. This evolution has led PT Gaia Solution to transform from conventional business practices to digital systems, particularly for managing assessments and interviews. The transformation aims to create a web application dedicated to handling assessment and interview operations at PT Gaia Solution. This study adopts the Software Development Life Cycle (SDLC) methodology using the waterfall model, which consists of the stages of requirements analysis, system design, implementation, testing, and evaluation. Application evaluation shows satisfactory results, with blackbox testing achieving a 100% success rate and high UI/UX satisfaction among users. The application was completed on time according to the set deadline and provides speed and performance that meet expectations. The outcome is a fully functional website that effectively addresses PT Gaia Solution's requirements for managing assessments and interview activities. By overcoming the limitations of the conventional system, this transformation boosts the overall efficiency and effectiveness of the company's processes. This success demonstrates that the implementation of SDLC with a waterfall approach can produce an effective web-based application as a form of transformation from conventional to digital systems at PT Gaia Solution, particularly for assessment and interview activities. This transformation can be immediately implemented and provides significant benefits to the company, including improved operational performance, data accuracy, and flexibility.

General trends in the demographic situation in the Republic of Kazakhstan: gender aspect

According to the operational data of the Bureau of national statistics of the agency of the Republic of Kazakhstan for Strategic Planning and reforms as of November 14, 2023, the population of Kazakhstan was 20,000,000 people. Despite the fact that in recent years there has been an increase in this indicator, it is impossible to neglect the presence of certain demographic problems in the country. Kazakhstan ranks 61st among 242 countries in the world population ranking. In turn, given the fact that the population density in the country is 7.3 people per 1 km2, experts say that in order to reach correspondence between the number of population and the size of the territory, it is necessary that this indicator is increased by no less than two times. Taking into account the fact that the average annual population growth in the Republic of Kazakhstan is 1.2–1.3%, it will take more than 70 years for the population to double. The main indicators that affect the demographic situation in the country include the population, its composition, birth and death rates, marriage and divorce rates, and migration conditions. In this regard, we consider it important to thoroughly study these indicators and, on the basis of this, take measures aimed at optimizing the demographic situation in the country. The purpose of the article is to develop suggestions for increasing the population of the country based on an in – depth analysis of the demographic situation of the Republic of Kazakhstan, including the gender aspect.

A bowtie model for fuel gas leakage incidents in an FPSO engine room

Safety and operational costs are of paramount importance in offshore facilities. Efforts to find new ways to reduce operational costs and minimize risks have led to the development of techniques in the field of safety. Routine checks and equipment maintenance are conducted based on either calendar periods or equipment uptime, using conditional monitoring, to prevent malfunctions through preventive maintenance (PM). PM is a key strategy for ensuring the integrity and process safety of safety barriers in offshore facilities. However, due to challenges in modeling dependencies, determining maintenance intervals, and updating belief in operational data, ineffective safety barriers can occur and lead to incidents. Excessive maintenance can also increase the risk of operational mistakes among workers. This study examines safety and operational issues associated with fuel gas leak events caused by various risk factors in Floating Production Storage and Offloading (FPSO) engine rooms using the bow-tie model. Within this context, safety barriers are defined, encompassing both preventive controls and mitigating measures that can be employed to minimize potential risk factors. Furthermore, the study underscores the importance of maintenance intervals for a specific set of safety barriers, necessitating periodic testing using Condition-Based Maintenance (CBM) tools for FPSO units.

Federated Bayesian network approach for cross-regional air pollution classification: a case study of the Beijing-Tianjin-Hebei region.

Although machine learning methods have enabled considerable progress in air quality assessment, challenges persist regarding data privacy, cross-regional data processing, and model generalization. To address these issues, we introduce an advanced federated Bayesian network (FBN) approach. By integrating federated learning, adaptive optimization algorithms, and homomorphic encryption technologies, we substantially enhanced the efficiency and security of cross-regional air quality data processing. The novelty of this research lies in the improvements implemented in federated learning for air quality data analysis, particularly in distributed model training optimization and data consistency. Through the integration of adaptive structural modification strategies and simulated annealing immune optimization algorithms, we markedly enhanced the structural learning accuracy of the Bayesian network, resulting in a 20% improvement in prediction accuracy. Moreover, employing homomorphic encryption ensured data transmission security and confidentiality. In our Beijing-Tianjin-Hebei case study, our method demonstrated a 15% improvement in air quality classification accuracy compared to conventional methods and exhibited superior interpretability in analyzing environmental factor interactions. We quantified complex air pollution patterns across regions and found that a 30% fluctuation in the air quality index correlated with NO2 concentrations. We also observed a moderate positive correlation between specific pollutant indicators in Hebei Province and Tianjin and changes in air quality. Additionally, the FBN exhibited better operational efficiency and data confidentiality than other machine learning models in handling large-scale and multisource environmental data. Our FBN approach presents a novel perspective for environmental monitoring and assessment, vital for understanding complex air pollution patterns and formulating future ecological protection policies.

The rotor as a sensor – observing shear and veer from the operational data of a large wind turbine

Abstract. This paper demonstrates the observation of wind shear and veer directly from the operational response of a wind turbine equipped with blade load sensors. Two independent neural-based observers, one for shear and one for veer, are first trained using a machine-learning approach and then used to produce estimates of these two wind characteristics from measured blade load harmonics. The study is based on a dataset collected at an experimental test site featuring a highly instrumented 8 MW wind turbine, an IEC-compliant (International Electrotechnical Commission) met mast, and a vertical profiling lidar reaching above the rotor top. The present study reports the first demonstration of the measurement of wind veer with this technology and the first validation of shear and veer with respect to lidar measurements spanning the whole rotor height. Results are presented in terms of correlations, exemplary time histories, and aggregated statistical metrics. Measurements of shear and veer produced by the observers are very similar to the ones obtained with the widely adopted profiling lidar while avoiding its complexity and associated costs.

A Wind Power Fluctuation Smoothing Control Strategy for Energy Storage Systems Considering the State of Charge

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS’s remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power’s grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS’s capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS’s ability to smooth wind power fluctuations.

Determining effective sampling frequencies for transient pressure events in water distribution systems optimal sampling for WDS transients

This study introduces a novel methodology for determining the optimal frequency for pressure data acquisition in water distribution systems (WDSs) in response to transient-flow events such as pipe ruptures. By leveraging actual design and operational data, we simulated potential rupture scenarios and their resultant pressure wave patterns through transient-flow analysis. Harmonic series modeling was used to identify significant periods that closely replicate the observed pressure-wave time series. This allowed the derivation of recommended data acquisition intervals tailored to each monitoring site within the network. The proposed approach facilitates comprehensive analysis of all possible hydraulic shock scenarios, which can provide insights into both the existing monitoring infrastructure and strategic planning in the design phase of the WDS. Although the methodology focuses on rupture events, its application can extend to various water-quality indicators and flow rates, albeit with consideration of their typically static behavior.

Integrating multiple data sources for improved flight delay prediction using explainable machine learning

Flight delays negatively impact costs, customer satisfaction, and revenue in the aviation industry. As a result, it is critical to identify the factors that cause flight delays for each airport, as they can vary depending on various attributes associated with their operations.This study proposes an explainable artificial intelligence (xAI) methodology for identifying the features that affect airport delays by integrating data from multiple sources and implementing explainable artificial intelligence. The methodology incorporates operational data, airport information, geographic data, and weather data combined and used to train a series of machine learning models. Furthermore, the SHAP and Sobol techniques are used to thoroughly analyze the features that influence flight delays for the specific case of the airport in Santiago, Chile.The results show that a linear discriminant analysis model is best suited for predicting flight delays in this specific case study, and the features that have the most significant impact on delays are the international flight status, average temperature at the destination airport, wind speed, and average temperature at Santiago airport.The proposed methodology could be applied by airlines that can collect data from multiple sources and conduct similar investigations, leading to the development of a decision support system to make better-informed decisions and reduce the impact of flight delays.

Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring

Anomaly detection is indispensable for ensuring the reliable operation of grid-connected photovoltaic (PV) systems. This study introduces a semi-supervised deep learning approach for fault detection in such systems. The method leverages a variational autoencoder (VAE) to extract features and identify anomalies. By training the VAE on normal operation data, a compact latent space representation is created. Abnormal observations, indicating faults, exhibit distinct feature vectors in this latent space. Multiple anomaly detection algorithms, including Isolation Forest, Epileptic Envelope, Local Outlier Factor, and One-Class SVM, are employed to discern normal and abnormal observations. This semi-supervised approach only requires fault-free data for training, without labeled faults, making it attractive in practice. A publicly available dataset, the Grid-connected PV System Faults (GPVS-Faults) dataset, which includes data from a PV plant operating in both maximum power point tracking (MPPT) and intermediate power point tracking (IPPT) switching modes, is used for evaluation. The proposed approach is assessed across various fault scenarios, such as partial shading, inverter faults, and MPPT/IPPT controller faults in boost converters. The outcomes underscore the effectiveness of VAE-based techniques in accurately identifying these faults, with accuracy rates reaching up to 92.90% for MPPT mode and 92.99% for IPPT mode, thus contributing to the robustness of fault detection in grid-connected PV systems.

Research on load prediction of low-calorific fuel fired gas turbine based on data and knowledge hybrid model

The high-precision load prediction technology plays a vital role in load control and health management for gas turbines. Low-calorific fuel fired gas turbines pose an especially significant challenge for load prediction due to the frequent and wide fluctuations of gas parameters. First, an improved hybrid-dimension physical-based model is proposed, which is corrected based on segmented operation data and integrates zero-dimensional thermodynamic knowledge of gas turbines with three-dimensional fluid dynamics simulation knowledge. Secondly, an optimal data-driven model is proposed through conducting a systematic comparative study on four popular machine learning models with two types of input variables feature extraction. Then, a novel hybrid model is proposed based on the improved physical-based model, optimal data-driven model, and the principle of minimizing hybrid model errors. The average relative prediction error of the hybrid model over the complete operation range is 0.68%. Compared with the physical-based model and the data-driven model, the proposed hybrid model can improve the prediction accuracy by 63% and 29%, respectively. As the high-calorific fuel fired gas turbine system is relatively simple and has a small range of fuel fluctuations, the method established in this paper can be extended to high-calorific fuel fired gas turbines, which is of great significance for promoting the dynamic balance of loads in a new type of electric power system based on new energy.