<jupyter_start><jupyter_text>This notebook will explore the use of the energy yield assessment (EYA) gap analysis method in OpenOA. This method attributes differences in an EYA-estimated and an operational assessment (OA) estimate of annual energy production (AEP; or net energy, P50). Differences in availability loss estimates, electrical loss estimates, and turbine ideal energy estimates are analyzed. The latter metric incorporates many aspects of an EYA, including the wind resource estimate, wake loss estimate, turbine performane, and blade degradation.<jupyter_code>import operational_analysis
import matplotlib.pyplot as plt
from operational_analysis.methods import eya_gap_analysis<jupyter_output>C:\Users\moptis\AppData\Local\Continuum\anaconda2\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
  from pandas.core import datetools
WARNING:Fiona:GDAL data files not located, GDAL_DATA not set
WARNING:Fiona:PROJ data files not located, PROJ_LIB not set<jupyter_text>Setup some arrays with EYA and OA data, in required order<jupyter_code>oa_dict = [448., 0.0493, 0.012, 477.8] 
# AEP (GWh/yr), availability loss (fraction), electrical loss (fraction), turbine ideal energy (GWh/yr)

eya_dict = [467.0, 597.14, 0.062, 0.024, 0.037, 0.011, 0.087] 
# AEP (GWh/yr), availability loss (fraction), electrical loss (fraction), turbine performance loss (fraction)
# blade degradation loss (fraction), wake loss (fraction)<jupyter_output><empty_output><jupyter_text>Create gap analysis object<jupyter_code>gap_anal = eya_gap_analysis.EYAGapAnalysis(plant = 'NA', eya_estimates = eya_dict, oa_results = oa_dict)<jupyter_output>INFO:operational_analysis.methods.eya_gap_analysis:Initializing EYA Gap Analysis Object<jupyter_text>Create waterfall plot<jupyter_code>gap_anal.run()
plt.show()<jupyter_output>INFO:operational_analysis.methods.eya_gap_analysis:Gap analysis complete
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