Energy

Matlab codes and data

This file defines the look-up tables of the resistance and open-circuit voltage of the battery cell and supercapacitor cell. This file is also used as the inputs for running the standalone dynamic programming optimization with either "DP_for_battery degradation.m" or "DP_for_battery replacements and HESS costs".

Raw data is the original data of our research. 10mins-realdata is the real energy consumption data of the prediction experiment. 10mins-predictiondata is the prediction data of four models.

• 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.

The single line diagrams of the three cases and all the test data employed in the present work.

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: bas_salaraskari@yahoo.com s_askari@aut.ac.ir 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.

The raw data and processed data

The dataset collected from secondary sources included World Bank Indicators and UNDP in 2018,

This is a two-column matrix of the drive cycle "T-US06".The first column represents the time in seconds and the second column represents the vehicle speed in miles per hour.

900 samples for data-driven analysis and optimization of EHIDiC