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Application of a control algorithm for wind speed prediction and active power generation

RENEWABLE ENERGY 2005年第4期30卷 523-536页

作者： Flores, P Tapia, A Tapia, G Univ Basque Country EUP UEP D Syst Engn & Control Dept San Sebastian 20018 Spain

The main objective of the work described in this paper is to offer a new method of prediction of wind speeds, whilst aware that the method develops predictions in time-scales that can vary from a few minutes to an hour. This is needed because wind energy generation is increasing its participation in energy distribution and has to compete with other energy sources that are not so variable in terms of generated active power. It is important to consider that active power demand can vary quite rapidly and different sources of electricity generation must be available. In the case of wind energy, wind speed predictions are an important tool to help producers make the best decisions when selling the energy produced. These decisions are crucial in the electricity market, because of the economic benefits for producers and consequently their profitability, depends on them. The algorithm presented in this paper is based on an artificial neural network and two types of wind data have been used to test the algorithm. In the first, data was collected from a not very windy area;in the second data was collected from a real wind farm located in Navarre (North of Spain), and the values vary from very low to high speeds. Although the algorithm was not tested with typical wind speed values measured on offshore wind farm applications, it can be concluded from the first set of results presented in this paper that the algorithm is valid for estimating average speed values. Finally, a generic algorithm for the active power generation of a wind farm is presented. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： short-time wind power forecasting prediction algorithm artificial neural network wind power generation doubly fed induction machine

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Improving bio-mass production, energy efficiency, and carbon dioxide absorption in microalgae buildings and urban landscapes with prediction algorithms

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CASE STUDIES IN THERMAL ENGINEERING 2024年 61卷

作者： Borna, Mohadese Yeganeh, Mansour Tarbiat Modares Univ Dept Architecture Architectural Design Modeling & Fabricat Lab Tehran Iran Tarbiat Modares Univ Dept Architecture Digital Architecture & Artificial Intelligence Lab Tehran Iran

This research aims to identify and classify the types of microalgae and investigate their growth and control conditions and their optimal use for construction purposes and urban landscapes. How to use types of microalgae in climates, types of buildings, and geographical directions and fronts of buildings requires comprehensive knowledge of microalgae. Different microalgae can be more beneficial for different purposes, including, shading on buildings and translucent surfaces, insulation used, and biomass production. Also, estimating the intensity of light radiation to different levels and climates is necessary to provide better growth conditions and higher efficiency. With a comprehensive review and analysis of these conditions, this article has presented two innovative methods to match the required characteristics of each microalga with the existing conditions of the urban landscape and buildings so that it can be determined in different parts of the building. Moreover, which microalgae are most effective for urban landscapes in different climates, on surfaces with different slopes and angles, and for different purposes? Finally, a new perspective and vision for research and further applications of microalgae at the scale of the building and urban landscapes have been presented.

关键词： prediction algorithm Cultivation method Light intensity Conceptual analysis

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Non-invasive menage a trois for the prediction of high-risk varices: stepwise algorithm using lok score, liver and spleen stiffness

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LIVER INTERNATIONAL 2015年第2期35卷 317-325页

作者： Stefanescu, Horia Radu, Corina Procopet, Bogdan Lupsor-Platon, Monica Habic, Alina Tantau, Marcel Grigorescu, Mircea Reg Inst Gastroenterol & Hepatol Hepatol Unit Cluj Napoca 400162 Romania Iuliu Hatieganu Univ Med & Pharm Med Clin 3 Cluj Napoca Romania Iuliu Hatieganu Univ Med & Pharm Dept Med Imaging Cluj Napoca Romania

Background & AimsLiver stiffness (LS), spleen stiffness (SS) and serum markers have been proposed to non-invasively assess portal hypertension or oesophageal varices (EV) in cirrhotic patients. We aimed to evaluate the performance of a stepwise algorithm that combines Lok score with LS and SS for diagnosing high-risk EV (HREV) and to compare it with other already-validated non-invasive methods. MethodsWe performed a cross-sectional study including 136 consecutive compensated cirrhotic patients with various aetiologies, divided into training (90) and validation (46) set. Endoscopy was performed within 6months from inclusion for EV screening. Spleen diameter was assessed by ultrasonography. LS and SS were measured using Fibroscan. Lok score, platelet count/spleen diameter ratio, LSM-spleen diameter to platelet ratio score and oesophageal varices risk score (EVRS) were calculated and their diagnostic accuracy for HREV was assessed. The algorithm classified patients as having/not-having HREV. Its performance was tested and compared in both groups. ResultsIn the training set, all variables could select patients with HREV with moderate accuracy, the best being LSPS (AUROC=0.818;0.93 sensitivity;0.63 specificity). EVRS, however, was the only independent predictor of HREV (OR=1.521;P=0.032). The algorithm correctly classified 69 (76.66%) patients in the training set (P<0.0001) and 36 (78.26%) in the validation one. In the validation group, the algorithm performed slightly better than LSPS and EVRS, showing 100% sensitivity and negative predicted value. ConclusionThe stepwise algorithm combining Lok score, LS and SS could be used to select patients at low risk of having HREV and who may benefit from more distanced endoscopic evaluation.

关键词： high-risk oesophagealvarices liver stiffness prediction algorithm spleen stiffness

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Application of an Optimized Support Vector Regression algorithm in Short-Term Traffic Flow prediction

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2022年第6期18卷 719-728页

作者： Ai, Ruibo Li, Cheng Li, Na Qiqihar Univ Coll Sci Qiqihar Peoples R China Qiqihar Univ Coll Comp & Control Engn Qiqihar Peoples R China Qiqihar Univ Publ Foreign Language Teaching & Res Dept Qiqihar Peoples R China

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimiza-tion SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

关键词： Artificial Bee Colony algorithm Optimization prediction algorithm Short -time Traffic Flow Support Vector Regression

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ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and algorithm for prediction of Their Inhibitory Efficacy

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G3-GENES GENOMES GENETICS 2017年第9期7卷 2931-2943页

作者： Monga, Isha Qureshi, Abid Thakur, Nishant Gupta, Amit Kumar Kumar, Manoj CSIR Inst Microbial Technol Bioinformat Ctr Sect 39A Chandigarh 160036 India

Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://***/servers/aspsirna/) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar. ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Eff(mut)) and wild-type allele (Eff(wild)) with one mismatch by ASPsiPred(SVM) and ASPsiPred(matrix), respectively. In ASPsiPred(SVM), 922 unique ASP-siRNAs with experimentally validated quantitative Eff(mut) were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson's correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Eff(mut) against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPred(matrix) was constructed from rule-based studies describing the effect of single siRNA: mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAi-base

关键词： allele-specific siRNA ASPsiDb ASPsiPred genetic disease database prediction algorithm

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Synthesis and analysis of prediction errors and error fusion based prior for prediction algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 19835-19847页

作者： Kumar, Neeraj Chakravarti, Ashish Kumar Singh, Yashveer Nagarjuna Coll Engn & Technol Dept Elect & Commun Bengluru Karnataka India Pranveer Singh Inst Technol Dept Informat Technol Kanpur UP India ABES Engn Collage Dept Informat Technol Gzaziabad UP India

In this paper, we propose a simple and efficient post-processing algorithm to improve the accuracy of prediction based, computationally simple image reconstruction algorithms. The proposed algorithm works where there is a need to restore the missing pixels (such as interpolation, deinterlacing, sub-pixel rendering, denoising, and demosaicing). In this work, we formulate the post-processing stage as a Maximum-a-Posteriori (MAP) estimation problem. Interestingly, we find that prediction errors of a missing pixel and its neighboring pixels are also correlated, which can be utilized to improve the prediction accuracy. Therefore, we propose an efficient way of calculating prior information by estimating synthetically created prediction errors from the four connected neighbors and fuse these prediction errors based on the corresponding activity levels. Experiments demonstrate that the proposed method can significantly improve the performance of existing computationally simple prediction algorithms in terms of both objective and subjective quality with a slight increment of the computational requirement.

关键词： Image processing Error fusion Maximum-a-posteriori prediction algorithm

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Use of Neural Network Based prediction algorithms for Powering Up Smart Portable Accessories

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NEURAL PROCESSING LETTERS 2021年第1期53卷 721-756页

作者： Qadir, Zakria Ever, Enver Batunlu, Canras Middle East Tech Univ Sustainable Environm & Energy Syst Northern Cyprus CampusMersin 10 TR-99738 Guzelyurt Turkey Middle East Tech Univ Comp Engn Northern Cyprus CampusMersin 10 TR-99738 Guzelyurt Turkey Middle East Tech Univ Elect & Elect Engn Northern Cyprus CampusMersin 10 TR-99738 Guzelyurt Turkey

Emerging Trends in the use of smart portable accessories, particularly within the context of the Internet of Things (IoT), where smart sensor devices are employed for data gathering, require advancements in energy management mechanisms. This study aims to provide an intelligent energy management mechanism for wearable/portable devices through the use of predictions, monitoring, and analysis of the performance indicators for energy harvesting, majorly focusing on the hybrid PV-wind systems. To design a robust and precise model, prediction algorithms are compared and analysed for an efficient decision support system. Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) prediction algorithms are used to develop a Shallow Neural Network (SNN) for time series prediction. The proposed SNN model uses a closed-loop NARX recurrent dynamic neural network to predict the active power and energy of a hybrid system based on the experimental data of solar irradiation, wind speed, wind direction, humidity, precipitation, ambient temperature and atmospheric pressure collected from Jan 1st 2015 to Dec 26th 2015. The historical hourly metrological data set is established using calibrated sensors deployed at Middle East Technical University (METU), NCC. The accessory considered in this study is called as Smart Umbrella System (SUS), which uses a Raspberry Pi module to fetch the weather data from the current location and store it in the cloud to be processed using SNN classified prediction algorithms. The results obtained show that using the SNN model, it is possible to obtain predictions with 0.004 error rate in a computationally efficient way within 20 s. With the experiments, we are able to observe that for the period of observation, the energy harvested is 178 Wh/d, where the system estimates energy as 176.5 Wh/d, powering the portable accessories accurately.

关键词： Energy harvesting Renewable energy sources Shallow neural network prediction algorithm Smart umbrella system Raspberry pi Cloud computing

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Cut-in Critical Level prediction via Simulation Based Time-to-Collision algorithm 25

Cut-in Critical Level Prediction via Simulation Based Time-t...

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25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Hu, Yunhao Dai, Mian Wang, Kui Drugge, Lars KTH Royal Inst Technol Sch Engn Sci Stockholm Sweden ZF Automot EE Commercial Vehicle ADAS Dev Tokyo Japan

ISBN: (纸本)9781728189567

Cut-in critical level prediction is of vital importance to Advanced Driving Assistance system (ADAS) to fulfil regional requirements and to increase safety. Time-to-Collision is the key component for critical levels of cut-in scenario. Hence, a new simulation based Time-to-Collision (TTC) calculation algorithm is firstly introduced in this paper. For the purpose of cut-in critical level prediction, the values of TTC in c.a 2000 cut-in cases are calculated, which are used to train a novel machine learning based cut-in critical level prediction method. The goal of ADAS functions development is to perform as a sophisticated driver, especially in dealing with risks. Thus, the correlation coefficient between ego vehicle deceleration and TTC could be used to evaluate the performance of different TTC calculation methods. In order to validate the superiority of simulation based TTC calculation algorithm, the Pearson correlation coefficient is calculated for the simulation based TTC and the TTC calculated by the traditional method, which are 0.7882 and 0.1357, respectively. Through enough valid regional cut-in samples trained prediction algorithm, TTC could be estimated accurately and effectively, i.e., the accuracy reaches 92%. To the best of the author's knowledge the simulation based TTC calculation method and the cut-in critical level prediction learning algorithm are new contributions in ADAS field.

关键词： Cut-in TTC Simulation-based method prediction algorithm

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Motion prediction of Lung Tumor using Predicted Error-based Normalized Least Mean Square algorithm

Motion Prediction of Lung Tumor using Predicted Error-based ...

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11th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Hung, Lephan Choi, Hongseok Park, Sukho Park, Jong-Oh Ko, Seong Young Chonnam Natl Univ Grad Sch Dept Mech Engn Gwangju South Korea Chonnam Natl Univ Sch Mech Engn Gwangju South Korea

ISBN: (纸本)9781479939794

In robotic radiotherapy, one of the problems is systematic latencies between the acquisition of the target position and the mechanical response of the system to follow the target position. To compensate the aforementioned latencies while tracing the tumor motion, an accurate algorithm to predict these latencies is therefore required. The prediction algorithm computes the future target position. In this study, we have analyzed the accuracy of three algorithms that predict tumor positions with sufficient lead time to compensate these systematic latencies. The motions have been analyzed for predictability up to 400ms in advance using Least Mean Square (LMS) prediction, Normalized Least Mean Square (NLMS) prediction and the proposed algorithm, named as a Predicted Error-based Normalized Least Mean Square (PE-NLMS) prediction. The performance of three prediction algorithms is evaluated using three real breathing signal data in 30 Hz sampling rate. The results show that the PE-NLMS is outperformed with respect to RMSE by all other algorithms and does not require too much parameter adjustment. The simulation showed that it Jitter needs to be improved for better performance.

关键词： prediction algorithm lung tumor motion respiratory compensation least mean square

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Improved prediction of Markov Chain algorithm for Indoor Temperature

Improved Prediction of Markov Chain Algorithm for Indoor Tem...

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3rd International Symposium on Computer, Consumer and Control (IS3C)

作者： Yang, Lei Shang, Liqun Yang, Lin Xi An Univ Sci & Technol Coll Elect & Control Engn Xian Shanxi Province Peoples R China State Grid Weinan Power Supply Co Inst Econ & Technol Weinan Shanxi Province Peoples R China

ISBN: (纸本)9781509030712

Aiming at the problem of Current indoor temperature monitor system unable to predict future temperature changes accurately, proposed an improved algorithm based on Markov chain model and analyzed the shortcomings of the one step transition state calculation method of the traditional Markov chain model. During the period of March to April 2015, measured the temperature data of a building located in Xi'an by indoor temperature monitor system and build prediction model, then compared the improved model prediction results with traditional prediction model. The results show that improved algorithm is better than traditional Markov chain model.

关键词： indoor temperature prediction algorithm time series markov chain

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