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lab notebook
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{
"metadata": {
"name": "2013-03-11"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#2013-03-11\n",
"\n",
"# Timeseries comparison\n",
"\n",
"The model we are building will output a timeseries of overall power consumption based on the extrapolated survey data. To ensure the usefulness of the output timeseries, we will compare it to the measured microgrid power consumption data. There are several ways to make these comparisons:\n",
"\n",
"## Distribution\n",
"\n",
"By comparing the two distributions of power readings, we can ensure that we are replicating the proportion of time that the system delivers power at the various levels. This could be done with the Wilcoxon/Mann-Whitney or similar tests.\n",
"\n",
"## Frequency Content\n",
"\n",
"It may be important to capture the time variation of the time series since the rate of change of power delivery can affect the performance of the generation system. Comparing the frequency spectra of the two timeseries is one way to make this comparison. I'll have to do further research to find comparison techniques for spectra.\n",
"\n",
"## Correlation\n",
"\n",
"This is most useful when we are trying to determine the temporal relation or predictive power between two timeseries. At first glance, this type of test may be less important since time-domain equivalence may not be extremely important.\n",
"\n",
"## RMS difference\n",
"\n",
"This technique would be most useful when trying to achieve a recreation of the original time series. If we are interested in only statistical resemblance, this may not be a useful metric."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"%load_ext octavemagic"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%octave -o p_table,power_tot\n",
"\n",
"run portapiment_grid_20130306.m"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I change from a stacked array to one long timeseries. This assumes each time series is along a column. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"pdf = pd.Series(p_table.flatten(order='F'))\n",
"pdf.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<matplotlib.axes.AxesSubplot at 0x44d5dd0>"
]
},
{
"output_type": "display_data",
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3y9XC3EtWdXj/B3nSd2MNQQhjUU3eW3xRg9+40d72vOWbc/ARU/a1wkLqtmVE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\nI0os3+0Y/OHDYu35KYbt5xi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u7cm+1dTUYMuWLRg7dmyg9uHEiRPIz89HOBxuCzcFRf9t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\n"
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here I remove the NaN to compress the series to allow an fft computation. This concatenation does introduce some high frequency content."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p_dna = pdf.dropna().values\n",
"plot(p_dna)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x491ed90>]"
]
},
{
"output_type": "display_data",
"png": 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1aoHXs8Erw6EqB0rZKRsvZq0clomZrcx2xZK3clsr2JkUY9s2ad5QK1htOQal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\n6VZ8vXVgvnU6deqEkSNHYu3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HXlR5ApWXlyMWiwEAYrEYysrKAABvv/02JkyYgPT0dGRl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\ngg==\n"
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distribution\n",
"\n",
"The distribution appears skewed and also has a flat shelf on the low end. If real, this shelf indicates a period of time where the generator is operating well below its operating point and it will be important to capture that behavior."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pdf.hist(bins=50)\n",
"ylabel('Frequency')\n",
"xlabel('Power Level')\n",
"title('Portapiment Power Histogram')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<matplotlib.text.Text at 0x4d0f410>"
]
},
{
"output_type": "display_data",
"png": 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6VrRhdS3mSTNhfingxA5gEjl8P+WQ0RJGO4d3330Xx44d47cU/Pz8cO3aNasH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\nZ8OGDXrfP0Pzt3f5pr9+Q49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}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Frequency content\n",
"\n",
"Most of the energy seems to be concentrated in the high frequency part of the spectra with relatively small contributions in the low frequency areas. I'm somewhat surprised by this since the power variations are so significant over short time frames. This will need more investigation."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# fft is a numpy library\n",
"p_dna = pdf.dropna().values\n",
"pfft = fft.fft(p_dna)\n",
"pfft = fft.fftshift(pfft)\n",
"semilogy(abs(pfft))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[<matplotlib.lines.Line2D at 0x48ae5d0>]"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
}
],
"metadata": {}
}
]
}
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