Gas networks simulation from disaggregation of low frequency nodal gas consumption

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: [e-mail] [e-mail] 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.尊敬的研究者： 感谢您使用本代码与数据集。本文将阐释我发表于《Energy》的论文《Gas networks simulation from disaggregation of low frequency nodal gas consumption》中提出的时间序列分解（Time Series Disaggregation, TSD）方法的运作原理。论文中提及的全部数据集与TSD方法的配套代码均已包含于此。如有任何疑问，可通过以下邮箱联系我：[e-mail]、[e-mail]。 顺颂安祺 S. Askari 打开名为"Gas networks simulation from disaggregation of low frequency nodal gas consumption"的文件夹，您将找到如下所述的代码、函数与数据集。 ### 代码与函数 1. **"gnetplot"**：该函数用于绘制燃气网络拓扑图。 2. **"TSD-UnrelatedTimeSeries"**：该代码用于对无关时间序列进行分解。 3. **"TSD-RelatedTimeSeries"**：该代码用于对相关时间序列进行分解。 4. **"NetworkSolution"**：该代码用于加载燃气网络属性与节点燃气消耗量，随后求解网络控制方程，并逐日计算网络节点压力。 ### 数据集 1. **"EX1"**：该数据集包含单条时间序列（对应论文中图3内容）。 2. **"EX2"**：该数据集包含6条相关时间序列（对应论文中图4内容）。 3. **"EX3"**：该数据集包含140条相关时间序列。 4. **"EX3Solved"**：该数据集包含"EX3"及其求解结果。本质上，这些数据是将"TSD-RelatedTimeSeries"应用于"EX3"后得到的结果。 5. **"GasNetworkProperties"**：该数据集包含论文中研究的燃气网络的属性信息，对应论文中图1内容。 6. **"Results"**：该文件包含论文中给出的部分结果与图表。 ### 代码使用指南 请使用MATLAB打开以下任一代码："TSD-UnrelatedTimeSeries"、"TSD-RelatedTimeSeries"或"NetworkSolution"。各代码的使用说明如下： 1. **"TSD-UnrelatedTimeSeries"**：该代码用于对单条或多条无关时间序列进行分解。代码第15行用于加载待分解的数据，您仅需在`load`指令后输入数据集名称即可完成数据加载。"EX1"数据集适用于该代码。按下`Ctrl+Enter`即可运行代码。若需使用自定义数据集，请按照"EX1"示例数据集的格式整理数据。根据论文中的理论，数据应包含自变量矩阵与无关时间序列矩阵，其中$N_h$为高频数据点数，$N_l$为低频数据点数，$n_x$为自变量个数，$n_u$为无关时间序列个数。向量$oldsymbol{y}^h$为原始高频数据，其聚合后可得到低频数据。完成分解后，可将得到的高频重构数据与原始数据进行对比，以验证所提TSD方法的精度。