FWEP2

Motivation

Proposal

wind_speed:
  name: wind_speed
  desc: wind speed
              'type':'weibull',
              'A':4.0,
              'k':2.0,
              'max':25.0,
              'min':4.0,
              'mean': None}

wind_speed = {'name':'wind_speed', 'desc':'wind speed distribution fit with parameter uncertainty', 'type':'weibull', 'A':{'type':'gaussian', 'mean':0.0, 'sigma':10.0}, 'k':{'type':'gaussian', 'mean':0.0, 'sigma':10.0}, 'max':25.0, 'min':4.0, 'mean': None}

wind_speed = { 'name':'wind_speed', 'desc':'wind speed', 'type':'weibull', 'A':4.0, 'k':2.0, 'max':25.0, 'min':4.0, 'mean': None, 'uncertainty':{ 'type':'gaussian', 'mean':0.0, 'sigma':10.0} }

wind_direction = {'name':'wind_direction', 'desc':'wind direction', 'type':'float'}

wind_directions = { 'name':'wind_directions', 'desc':'wind directions with measurement uncertainty', 'type':'array', 'value':array([]), 'uncertainty':{ 'type':'gaussian', 'mean':0.0, 'sigma':10.0 } }

wt_powers = {'name':'wt_powers', 'desc':'wind turbine powers', 'type':'array'} wt_thrusts = {'name':'wt_thrusts', 'desc':'wind turbine thrusts', 'type':'array'} wp_power = {'name':'wp_power', 'desc':'wind plant power', 'type':'float'}

wind_speed = { 'name':'wind_speed', 'desc':'wind speed distribution (truncated weibull)', 'type':'weibull', 'A':12.0, 'k':2.0, 'max':25.0, 'min':4.0, }

power = { 'name':'power', 'desc':'power', 'type':'KDE', 'value':array([...]), #dn3 array, with d nb dimensions, n the number of normals centers 'max':25.0, 'min':4.0, }

wind_direction = { 'name':'wind_direction', 'desc':'a fixed wind direction value', 'type':'float', 'value':10.0, 'units':'deg' }

wind_rose = { 'name':'wind_rose', 'desc':'local wind resource', 'type':'joint_distribution', 'joint_distribution':'binned_weibull', 'dimensions':['wind_speed', 'wind_direction'], 'wind_speed':'weibull', 'wind_direction':'cdf', 'values':np.array([]), 'columns':['wind_direction', 'frequency', 'A', 'k'] }

wind_atlas = { 'name':'wind_atlas', 'desc':('joint distribution built from a Copula for U,D,TI,S. U.', 'U is a truncated Weibull, TI is a lognormal and D&S are KDEs.'), 'dimensions':['wind_speed', 'wind_direction','TI','stability'], 'type':'copula', 'copula':{ 'type':'gaussian',#gumble... 'correlation':array([]),#4x4 correlation on the cdf-1 of the variables (might be more useful to store the inversed and determinant }, 'marginals':{ 'wind_speed':{ 'type':'weibull', 'A':12.0, 'k':2.0, 'max':25.0, 'min':4.0}, 'wind_direction':{ 'type':'KDE', 'value':array([...]), #n3 array, with 1 dimension, n the number of normals centers }, 'TI':{ 'type':'lognormal', 'mean':5.0, 'sigma':1.0, }, 'stability':{ 'type':'KDE', 'value':array([...]), #n3 array, with 1 dimension, n the number of normals centers } }, }

wind_atlas = { 'name':'wind_atlas', 'desc':'joint KDE distribution for U,D,TI,S with fixed gaussian uncertainty', 'type':'KDE', 'value':array([...]), #dn3 array, with d nb dimensions, n the number of normals centers 'dimensions':['wind_speed', 'wind_direction','TI','stability'], 'uncertainty':{ 'type':'multivariate_gaussian', 'mean':[0.0,...],#list(4) 'covariance':array([]),#array 4x4 } }

wind_atlas = { 'name':'wind_atlas', 'desc':'joint distribution for U,D,TI,S with a probability uncertainty function of the inputs', 'type':'KDE', 'value':array([...]), #dn3 array, with d nb dimensions, n the number of normals centers 'dimensions':['wind_speed', 'wind_direction','TI','stability'], 'uncertainty':{ 'type':'KDE', 'value':array([...]), #dn3 array, with d nb dimensions, n the number of normals centers 'dimensions':['wind_speed', 'wind_direction','TI','stability'], } }