Contributors: Mototsugu Fukushige
Data for: Robust Model Predictive Control for Optimal Energy Management of Island Microgrids with Uncertainties
Contributors: yan zhang
... the primary data used in the simulation
Contributors: Slimane Chala
... • Fabrication of ZnO film by sol–gel using zinc acetate decomposition • Extraction of the optical parameters to be used in modeling a ZnO/p-Si solar. • This is to elucidate the solar cell weak performance • Defects in the ZnO bulk and the surface recombination velocity in the ZnO/p-Si interface were responsible. • The simulation led to comparable values between simulation and measurement of J-V characteristics.
Contributors: Violette Mounier, Luis Eric Olmedo, Jürg Schiffmann
... Here are gathered the different polynomial functions that estimate the performance of a small scale radial inflow turbine function of specific speeds (ns), specific diameters (ds) and pressure ratio (PR). Five sets of functions performed with varying shroud to tip radius ratio (epsilon=r6s/r4=[0.5 0.6 0.7 0.8]) are presented. These polynoms were built with R134a, but as minor differences were found with the use of another organic fluid (R245fa, R600a, R152a), these maps are valid for different working fluids. For more information about the methodology of the map, its boundaries and other features, please refer to the following paper : "SMALL SCALE RADIAL INFLOW TURBINE PERFORMANCE AND PRE-DESIGN MAPS FOR ORGANIC RANKINE CYCLES, V.Mounier, L.E. Olmedo, J. Schiffmann, Energy, 2017"
Dataset of A transient model for temperature prediction in a salt-gradient solar pond and the ground beneath it
Contributors: Jose Amigo, Francisco Meza, Francisco Suarez
... These datasets correspond to the calibration and validation periods of the model presented in the paper "A transient model for temperature prediction in a salt-gradient solar pond and the ground beneath it" by J. Amigo, F. Meza and F. Suárez.
Contributors: Salar Askari Lasaki
... Dear Researcher, Thank you for using these codes and datasets. I explain how Time Series Disaggregation (TSD) methods introduced in my paper "Gas networks simulation from disaggregation of low frequency nodal gas consumption" published in Energy, works. All datasets mentioned in the paper accompanied with codes of TSD methods are included. If there is any question feel free to contact me at: firstname.lastname@example.org email@example.com Regards, S. Askari Opening the folder "Gas networks simulation from disaggregation of low frequency nodal gas consumption" you'll find some Codes, Functions, and Datasets which are described as follows. Codes and Functions: 1. "gnetplot": This function plots gas networks. 2. "TSD-UnrelatedTimeSeries": This code disaggregates unrelated time series. 3. "TSD-RelatedTimeSeries": This code disaggregates related time series. 4. "NetworkSolution": This code loads the network properties and nodal consumption and then solves the network governing equations and computes nodal pressure of the network for each day. Datasets: 1. "EX1": This dataset includes one time series (It is shown in Fig. 3 of the paper.). 2. "EX2": This dataset includes six related time series (It is shown in Fig. 4 of the paper.). 3. "EX3": This dataset includes 140 related time series. 4. "EX3Solved": This dataset includes the dataset "EX3" and its solution. In fact these data are results of applying "TSD-RelatedTimeSeries" to the data "EX3". 5. "GasNetworkProperties": This datasets includes properties of the gas network studied in the paper and shown in Fig. 1. 6. "Results": This file includes some of the results and graphs given in the paper. Guidelines for the codes: Open one of the codes "TSD-UnrelatedTimeSeries", "TSD-RelatedTimeSeries", and "NetworkSolution" using MATLAB. Guidelines for working with each code are as follows. 1. "TSD-UnrelatedTimeSeries": This code disaggregates single or multiple unrelated time series. Line 15 of the code loads the data to be disaggregated. You just need to type name of the dataset after "load" to load the data. The dataset EX1 is for this code. You need to press Ctrl+Enter to run the code. For your own dataset, arrange the data as the sample dataset EX1. According to the theory given in the paper, the data should include matrix of independent variables and matrix of unrelated time series where is number of high frequency data, is number of low frequency data, is number of independent variables, and is number of unrelated time series. The vector is the original high frequency data from which is constructed by aggregation. After disaggregation, the resulted high frequency data can be compared with its original version to verify accuracy of the proposed TSD method.