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Day-Ahead Solar Power Forecasting Using Artificial Neural Network with Outlier Detection
Published in Mohan Lal Kolhe, Kailash J. Karande, Sampat G. Deshmukh, Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications, 2023
D. Janith Kavindu Dassanayake, M.H.M.R.S. Dilhani, Sandun Y. Konara Konara Mudiyanselage, Mohan Lal Kolhe
Linear interpolation is utilized to discover the values between two data points. A simple method is applied to connect the points by the use of straight-line segments. Each segment bounded by two points can be interpolated independently. The data points that need to be replaced are supplanted with new values with a function found by analysing the dataset. The equation for the interpolation which is used in this study is expressed in Eq. (4.9): Pd(t)=Pd(t−1)+[(m2−m1)(m3−m1)](Pd(t+1)−Pd(t−1))
Guidelines for practical use of nonlinear finite element analysis
Published in Paulo B. Lourenço, Angelo Gaetani, Finite Element Analysis for Building Assessment, 2022
Paulo B. Lourenço, Angelo Gaetani
In general, when approaching a nonlinear analysis, some parameters can be directly investigated while others are adopted according to codes of practice or literature. If not directly investigated, the analyst should pay due attention when referring to identical materials (or within a certain degree of similitude). For instance, dealing with masonry, mechanical properties differ according to the overall morphology of the structural element and to the nature of components. Additionally, mechanical parameters can be derived from regression curves or tables, the applicability of which is related to a specific range of input parameters. In other words, interpolation (within the original range) is generally allowed, but careful attention should be paid to extrapolation (beyond the original range). If the material behaviour cannot be determined accurately, varying the mechanical parameters within a plausible interval can help understanding their influence in the structural response. In the case important sensitivity to the input is found, further experimental or in situ investigations on the parameters are advisable. If this is not possible, higher degree of caution may need to be adopted when making conclusions from the analysis results.
Other means for estimating solar radiation at surface
Published in Lucien Wald, Fundamentals of Solar Radiation, 2021
Spatial interpolation is a mathematical process by which the radiation is estimated at a point and a time using a network of stations. Extrapolation is an extension of interpolation for which the considered site is not inside the zone defined by the stations, but outside. The mathematical process is often the same, and here I am deliberately confusing interpolation and extrapolation. A large number of interpolation techniques have been proposed. I just state the concept of interpolation and illustrate the principle of linear interpolation as an example (Figure 10.3). Given three measuring stations MS1, MS2, and MS3 for which the irradiations H1(t), H2(t), and H3(t) are known, the objective is to estimate the irradiation H(t) at location P surrounded by the three stations.
Automated system for performing pH-based titrations
Published in Instrumentation Science & Technology, 2023
Naga P. D. Boppana, Robyn A. Snow, Paul S. Simone, Gary L. Emmert, Michael A. Brown
For automated titrations, a 15 mL drinking water sample was transferred to a 30 mL beaker using a volumetric pipet. The sample was titrated to a pH value of 4.3 using standardized 0.02 N HCl and the endpoint was determined potentiometrically. The endpoint volume was determined by linear interpolation. The linear interpolation approach is a simple method of estimating a value between two data points. An example of linear interpolation is shown in Figure 3, where the volume of acid required to titrate the water sample to pH 4.3 (Vx) was calculated by: where pH1 and pH2 are pH values that bracket pH 4.3, and V1 and V2 are the added acid volumes at pH1 and pH2, respectively. The Vx volume at pH 4.3 was estimated by linear interpolation.
Implicit modelling and dynamic update of tunnel unfavourable geology based on multi-source data fusion using support vector machine
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2023
Binru Yang, Yulin Ding, Qing Zhu, Liguo Zhang, Haoyu Wu, Yongxin Guo, Mingwei Liu, Wei Wang
Take the first classifier as an example to show the modelling process. The first classifier was applied to other geological points, and the classification results are shown in Figure 11. In Figure 11(a), there are unfavourable geological points in the water-rich area in the blue cube, which are divided into two parts. In the two subplots of Figure 11(b), it can be observed that the recognised results are sometimes not continuous; i.e. the identified unfavourable geological points cannot be directly reconstructed into the implicit surface of the geological body. There are two main reasons for this. First, the accuracy of advanced geological prediction is affected by subjective human factors, such as the proficiency of on-site operation analysis professionals, their construction experience, and their tunnel engineering geological knowledge. Although this study adopts the comprehensive identification method and each geological point is affected by multiple data sources, this is a fundamental factor from which error cannot be eliminated. The second reason stems from the sparse advanced prediction data. This study adopted an interpolation method to interpolate the data of unknown points, resulting in additional errors. Given this situation and the continuity of geology, the identified unfavourable geology is delineated in the form of a convex hull, as shown in Figure 11. The convex hull can ensure that all unfavourable geological points are within this range and allow the appropriate increase and extension of unfavourable geological points.
Optimization of multi-frequency electromagnetic surveying for investigating waste characteristics in an open dumpsite
Published in Journal of the Air & Waste Management Association, 2022
Pornchanok Boonsakul, Sasidhorn Buddhawong, Komsilp Wangyao
Interpolation analysis is a statistical method used to predict the values of unknown data points based on known sample data. This method is currently applied in environmental science and geophysics fields such as altitude, depth, rainfall, and chemical concentration (Li and Heap 2014; Mariani and Basu 2015). In this, study, interpolation analysis was applied to decrease the limitations of LR analysis between ER data and synthetic ER data for different DOI; accordingly, the interpolation technique was applied to forecast the synthetic ER and ER data values for the same DOI. This study selected appropriate interpolation techniques based on the type of data (i.e., 1D or 2D data), with two interpolation analysis techniques applied (linear interpolation and bilinear interpolation) as follows: Linear interpolation is a common technique for creating new data points within the range of known data points using linear polynomials. This technique is suitable for one-dimensional data points and is effective for high-density data forecasting, especially for scientific applications (Li and Heap 2014; Mariani and Basu 2015). Thus, linear interpolation analysis was selected to forecast synthetic ER data at different DOI. This analysis technique allowed us to obtain synthetic ER values at the same depths as the 1D ER data, allowing the preliminary correlation between the datasets to be assessed. Linear interpolation analysis was calculated using the following relationship: