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Photovoltaic power forecasting using simple data-driven models without weather data

COMPUTER SCIENCE-RESEARCH AND DEVELOPMENT 2017年第1-2期32卷 237-246页

作者： Ordiano, Jorge Angel Gonzalez Waczowicz, Simon Reischl, Markus Mikut, Ralf Hagenmeyer, Veit Karlsruhe Inst Technol Inst Appl Comp Sci Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany

The present contribution offers evidence regarding the possibility of obtaining reasonable photovoltaic power forecasts without using weather data and with simple data-driven models. The lack of weather data as input stems from the fact that the constant obtainment of forecast weather data might become too expensive or that communication with weather services might fail, but still accurate planning and scheduling decisions have to be conducted. Therefore, accurate one-day ahead forecasting models with only information of past generated power as input for offline photovoltaic systems or as backup in case of communication failures are of interest. The results contained in the present contribution, obtained using a freely available dataset, provide a baseline with which more complex forecasting models can be compared. Additionally, it will also be shown that the presented weather-free data-driven models provide better forecasts than a trivial persistence technique for different forecast horizons. The methodology used in the present work for the data preprocessing and the creation and validation of forecasting models has a generalization capacity and thus can be used for different types of time series as well as different data mining techniques.

关键词： Forecasting data-driven models Photovoltaics Weather-free Energy Lab 2.0

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Displacement determination of concrete reinforcement building using data-driven models

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International Journal of Sustainable Built Environment 2017年第2期6卷 400-411页

作者： Khademi, Faezehossadat Akbari, Mahmoud Nikoo, Mehdi Civil Architectural and Environmental Engineering Department Illinois Institute of Technology Chicago IL United States Civil Engineering Department University of Kashan Kashan Iran Young Researchers and Elite Club Ahvaz Branch Islamic Azad University Ahvaz Iran

Decision making on buildings after the earthquake have always been a great concern of scientists. Safety concerns, possibility of using the building, repairing the building, and the rate of damage are some of the most vital factors that needs to be paid attention in immediate decision makings of the buildings. In order to determine the level of damage in the buildings, the maximum displacement of stories is one of the most important parameter that needs to be investigated. In this paper, a concrete frame with shear wall containing 4-stories and 4-bays has been designed for acceleration records of 0.1 g to 1.5 g and the rate of damage is determined. The total of 450 data with 6 input variables and one output variable is produced. The input parameters are defined as frequency, Vs, Richter, the distance from the earthquake epicentre (DEE), PGA, and acceleration, and the output parameter is defined as drift. With respect to this data set, three different data-driven models, i.e. Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression Model (MLR) are used to predict the displacements. Results indicate that Both the ANN and ANFIS model show great accuracies in estimating the displacements in concrete frame with shear wall. On the other hand, MLR model did not show acceptable accuracy in the same estimation purposes. Finally, the sensitivity analysis was performed on the data set and it was observed that the accuracy of the predictions highly depends on the number of input parameters. In other words, increasing the number of input parameters would result in the increase in the accuracy of the final prediction results. © 2017

关键词： Adaptive neuro-fuzzy inference system Artificial neural network data-driven models Displacement Multiple linear regression

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Development and validation of an effective and robust chiller sequence control strategy using data-driven models

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AUTOMATION IN CONSTRUCTION 2016年第May期65卷 78-85页

作者： Shan, Kui Wang, Shengwei Gao, Dian-ce Xiao, Fu Hong Kong Polytech Univ Dept Bldg Serv Engn Kowloon Hong Kong Peoples R China

Chiller sequence control significantly affects the efficiency and operation stability of plants with multiple chillers. However, in real practice, the energy efficiency is commonly sacrificed to avoid uncertainties. Also, the strategies found in literature may be too complicated to be used practically. An effective and robust strategy for centrifugal chiller plants is therefore developed. The strategy innovatively utilizes chillers inlet guide vane openings as the load, and more particularly the energy efficiency indicator. A validation of the use of such an indicator is conducted using the in situ measurements from the chiller plant in a high-rise building. The strategy is compared with two other commonly used strategies through tests. In the ideal condition (no measurement errors), the proposed strategy saves 3% of the energy comparing to the original strategy. When systematic errors exist in the cooling load measurements, energy performance of the plant is not affected when controlled by the proposed strategy. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Chiller sequence control Optimal chiller loading Robust control Measurement error Centrifugal chiller data-driven models

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Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers

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JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2025年 57卷

作者： Xu, Shujie Ye, Zhongnan Hsu, Shu-Chien Liu, Xiaoyi Zheng, Chunmiao Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Guangdong Hong Kong Joint Lab Soil & Groundwater P Shenzhen 518055 Guangdong Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Guangdong Prov Key Lab Soil & Groundwater Pollut C Shenzhen 518055 Peoples R China Eastern Inst Technol Eastern Inst Adv Study Ningbo Peoples R China

Study region: The study focuses on the 12 rivers across Hong Kong, that have been facing varying degrees of algal bloom risks. Study focus: data-driven models are extensively employed to forecast and discern the catalysts of algal blooms, leveraging high temporal resolution data to unveil intricate numerical relationships among indicators. The efficacy of these models can be compromised due to the low temporal resolution of water quality monitoring. This study presents a data-driven algal bloom alert model employing machine learning and data discretization techniques to solve the data issue, utilizing monthly water quality data of 12 rivers and daily weather data in Hong Kong. The critical factors associated with algal blooms were then identified by feature importance analysis and false negative analysis of the optimal performance model, guiding potential algal control strategies for Hong Kong. New hydrological insights for the region: models that integrate data discretization outperformed those using numerical normalization, showing higher recall scores and greater stability across selected algorithms (linear regression, support vector machine, random forest, and decision tree). Permutation importance analysis identified nitrogen compounds and rising temperatures as key triggers of algal blooms, while false negative analysis highlighted total phosphorus and flow as critical factors. This study offers a potential solution for prediction and forecasting without extensive datasets and emphasizes the importance of integrating domain knowledge in both prior and posterior analyses to enhance the interpretability of data-driven models in water science.

关键词： Algal blooms data-driven models data discretization Water management

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data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

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WATER RESEARCH 2024年 255卷 121499页

作者： Uddin, Md Galal Rahman, Azizur Taghikhah, Firouzeh Rosa Olbert, Agnieszka I. Univ Galway Sch Engn Galway Ireland Univ Galway Ryan Inst Galway Ireland Univ Galway MaREI Res Ctr Galway Ireland Natl Univ Ireland Galway Ecohydroinformat Res Grp EHIRG Civil Engn Galway Ireland Charles Sturt Univ Sch Comp Math & Engn Wagga Australia Charles Sturt Univ Gulbali Inst Agr Water & Environm Wagga Australia Univ Sydney Business Sch Camperdown NSW 2050 Australia Univ Galway Coll Sci & Engn Civil Engn Galway Ireland

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures. The performance metric (R-2) indicates that the model performance improved slightly (R-2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datase

关键词： data-driven models Water quality models IEWQI model data outliers Machine learning

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data-driven modelling with coarse-grid network models

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COMPUTATIONAL GEOSCIENCES 2024年第2期28卷 273-287页

作者： Lie, Knut-Andreas Krogstad, Stein SINTEF Digital Math & Cybernet POB 124 Blindern N-0314 Oslo Norway

We propose to use a conventional simulator, formulated on the topology of a coarse volumetric 3D grid, as a data-driven network model that seeks to reproduce observed and predict future well responses. The conceptual difference from standard history matching is that the tunable network parameters are calibrated freely without regard to the physical interpretation of their calibrated values. The simplest version uses a minimal rectilinear mesh covering the assumed map outline and base/top surface of the reservoir. The resulting CGNet models fit immediately in any standard simulator and are very fast to evaluate because of the low cell count. We show that surprisingly accurate network models can be developed using grids with a few tens or hundreds of cells. Compared with similar interwell network models (e.g., Ren et al., 2019, 10.2118/193855-MS), a typical CGNet model has fewer computational cells but a richer connection graph and more tunable parameters. In our experience, CGNet models therefore calibrate better and are simpler to set up to reflect known fluid contacts, etc. For cases with poor vertical connection or internal fluid contacts, it is advantageous if the model has several horizontal layers in the network topology. We also show that starting with a good ballpark estimate of the reservoir volume is a precursor to a good calibration.

关键词： data-driven models Model calibration Interwell network models

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Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

Challenges of the application of data-driven models for the ...

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European Control Conference

作者： Dionysios P. Xenos Olaf Kahrs Matteo Cicciotti Fernando Moreno Leira Nina F. Thornhill Department of Chemical Engineering Centre for Process Systems Engineering Imperial College London London SW7 2AZ UK Corporate Operational Excellence - Technical Process Optimisation BASF SE 67056 Ludwigshafen Germany Process Control Task Force BASF SE 67056 Ludwigshafen Germany Automation Technology - Advanced Process Control BASF SE 67056 Ludwigshafen Germany

ISBN: (纸本)9781509025923

The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data.

关键词： Challenges application data-driven models

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An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models

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JOURNAL OF HYDROLOGY 2016年 542卷 18-34页

作者： Taormina, R. Galelli, S. Karakaya, G. Ahipasaoglu, S. D. Singapore Univ Technol & Design Pillar Engn Syst & Design Singapore Singapore Middle East Tech Univ Dept Business Adm Ankara Turkey

This work investigates the uncertainty associated to the presence of multiple subsets of predictors yielding data-driven models with the same, or similar, predictive accuracy. To handle this uncertainty effectively, we introduce a novel input variable selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), specifically conceived to identify all alternate subsets of predictors in a given dataset. The search process is based on a four-objective optimization problem that minimizes the number of selected predictors, maximizes the predictive accuracy of a data-driven model and optimizes two information theoretic metrics of relevance and redundancy, which guarantee that the selected subsets are highly informative and with little intra-subset similarity. The algorithm is first tested on two synthetic test problems and then demonstrated on a real-world streamfiow prediction problem in the Yampa River catchment (US). Results show that complex hydro-meteorological datasets are characterized by a large number of alternate subsets of predictors, which provides useful insights on the underlying physical processes. Furthermore, the presence of multiple subsets of predictors and associated models helps find a better trade-off between different measures of predictive accuracy commonly adopted for hydrological modelling problems. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Input variable selection Information theory data-driven models Extreme learning machines Neural networks

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data-driven and physics-informed approaches for improving the performance of dynamic models of fluid film bearings

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TRIBOLOGY INTERNATIONAL 2024年 191卷

作者： Shutin, Denis Kazakov, Yuri Stebakov, Ivan Savin, Leonid Orel State Univ Dept Mechatron Mech & Robot Oryol Russia

Conventional numerical models of fluid film bearings (FFB) typically require significant amounts of computations to achieve acceptable calculation accuracy. This may cause many inconveniences, especially for computationally expensive rotor dynamics tasks. The study addresses general issues in building synthetic data-driven dynamic FFB models using machine learning methods, focusing on artificial neural networks (ANNs). In the study, surrogate models predicting journal bearing forces were built using a variety of approaches, including single- and multi-component ANN-based models, as well as so-called physics-informed neural networks (PINN). The work presents a comparative analysis of the considered approaches, primarily in the context of rotor dynamics calculations. It includes an assessment of the accuracy and performance of considered models, as well as the time spent on their creation. The results show that all tested solutions outperform numerical methods by order of magnitude or more, but it is difficult to talk about the overwhelming advantage of any one approach over others. When choosing a methodology for building surrogate FFB models, one should proceed from the requirements for the model, the benefits and shortcomings of certain methods. The work gives an idea of their relationships, including the influence of key hyperparameters of the methods on the properties of the resulting models. The results can be useful both for traditional calculations in the area of rotor dynamics, and for relatively new problems in the rotor systems, such as predictive analytics and optimal design systems, predictive controllers of active components of rotary machines.

关键词： Fluid film bearings Rotor dynamics Numerical models Machine learning data-driven models Artificial neural networks Physics-informed neural networks

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How well do light-use efficiency models capture large-scale drought impacts on vegetation productivity compared with data-driven estimates?

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ECOLOGICAL INDICATORS 2023年 146卷

作者： Lv, Yiming Liu, Jinxiu He, Wei Zhou, Yanlian Nguyen, Ngoc Tu Bi, Wenjun Wei, Xiaonan Chen, Hui China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Nanjing Univ Int Inst Earth Syst Sci Nanjing 210023 Jiangsu Peoples R China Chinese Acad Sci State Key Lab Remote Sensing Sci Jointly Sponsored Beijing 100854 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Beijing 210023 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing 210023 Jiangsu Peoples R China Hohai Univ Coll Hydrol & Water Resources State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing Peoples R China

Remote sensing driven light-use efficiency (LUE) models are an important tool for estimating vegetation gross primary productivity (GPP) and assessing climate change impacts on the terrestrial carbon cycle, but their ca-pacity in representing drought impact on GPP over large-scales is not fully explored. Here, we evaluated a suite of LUE models, i.e., VPM, TL-LUE, CASA, EC-LUE and MuSyQ, against two data-driven flux products (GOSIF-GPP and FluxSat), with the aids of environmental variables and eddy covariance flux measurements over North America. The two data-driven GPP products were firstly validated against with eddy covariance flux measure-ments, suggesting that they are able to reasonably indicate drought impacts at site scales. They were then used as a reference to evaluate these LUE models for indicating the drought impacts in 2011 and 2012 over North America at regional scales. In general, the simulated GPP by these models reasonably responded to droughts, but showed considerable differences in the seasonal anomalies for both amplitude and phase. Especially, these models consistently showed a clearly earlier trough (about 1 similar to 2 months) of flux reduction than those in data -driven models over both entire drought-impacted regions and individual ecosystems (mostly pronounced for grasslands), indicating a likely misrepresentation of drought legacy effects, which needs to be considered in developing new-generation LUE models. Among the grass-dominated drought areas, TL-LUE and VPM performed best in indicating the impacts of the 2011 and 2012 droughts on regional GPP, respectively, while EC-LUE showed a significant underestimation of the impacts in both events, which may be attributed to the different representations of water stress factors in models. This study could provide some useful hints toward improving LUE models in characterizing drought impacts on regional GPP.

关键词： Gross primary productivity (GPP) Light-use efficiency models data-driven models Drought impact Terrestrial carbon cycle

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