def RLCSimulation(folderName, inputType, unit, approachName, houseNumber, randomFlag):
'''
This method used to construct yaml file and construct the RLC result based on random.
str, str, int, str, [int, int, ...] => file
'''
# read CSV input files
Result = ReadCSV(folderName, inputType)
requests = list(Result[0])
ideal_type_load = modify_ideal_load(Result[1][-1], unit)
# write load profile files
getLoadProfileFiles(folderName, unit)
# get house layout and zip_loads
houseLayout = yamlCalcMethods.getHouseLayout(houseNumber, requests)
S_ZipList = yamlCalcMethods.getSZipList(len(houseLayout))
lengthMvList = yamlCalcMethods.getLengthMvList(len(houseLayout))
# get the load, which not perform RLC approach
original_loads = getOriginalObservedLoad(Result, unit, approachName)
# get the relationship between request name, and its last minute number
requestNameMinMap = yamlCalcMethods.getRequestNameMinMap(requests) # get the requestNameMinMap
# write yaml file, and return RLC result
random_observed_loads = writeYamlFileAndReturnRandomProbabilities(Result, houseNumber, requests, unit, folderName, approachName, houseLayout, requestNameMinMap, S_ZipList, lengthMvList)
# write the csv file which save original and RLC loads
write_output(ideal_type_load, original_loads, random_observed_loads, folderName, inputType, randomFlag)
print 'FINISH Random Output'
def calcRandStartTime(Result, unit, approachName, houseNumber):
"""
This method used to get the load information based on random number
for each request from each 'house'. Then start time of each house in different appliances
[request,...], int, string, int => {str : [float, float,...], ...}
"""
ideal_shiftable_load = modify_ideal_load(Result[1][-1], unit) # get ideal shift-able load
signal_load = list(ideal_shiftable_load) # make ideal shift-able load equal to signal load
## first of all, get requests from reading result
requests = Result[0]
requests = updateProfile(requests, unit) # update profile
requests = getProbDistribution(requests, signal_load, unit, approachName) # update probabilities
requestTypeNumberDict = getEachRequetsTypeNumber(Result[0]) # dict with key-value: requestName to this Type requests Number
requestTypeGroupDict = getRequestTypeGroupNumber(requestTypeNumberDict) # distribute the request types into four big groups
requestNameRequestsMap = orderRequests(requests) # use to save request with same request name
for requestTypeGroup in requestTypeGroupDict.keys():
if houseNumber <= requestTypeGroupDict.get(requestTypeGroup):
groupMultiplyMap = {}
for requestTypeGroup in requestTypeGroupDict.keys():
groupMultiplyMap[requestTypeGroup] = float(houseNumber) / requestTypeGroupDict[requestTypeGroup]
applianceDict = getRandStartTimeGreaterThanHouse(groupMultiplyMap, requestNameRequestsMap, requestTypeNumberDict, unit, houseNumber)
else:
applianceDict = getRandStartTimeLessThanHouse(requestNameRequestsMap, unit)
return applianceDict
def getOriginalObservedLoad(Result, unit, approachName):
'''
This method used to get the orginial observed load, without perform RLC approach.
Result, int, str => [[int, int, ]]
'''
requests = list(Result[0])
# get the start observed result
ideal_type_load = modify_ideal_load(Result[1][-1], unit)
initialResult = initialSituation(ideal_type_load, requests, unit, approachName)
original_observed_load = initialResult[1] # get the observed load without random approach
original_loads = [original_observed_load]
return original_loads
def main():
## input information
folder_name = 'Experiment2_1'
input_type = 'ideal_shiftable_load'
unit = 5
approach_name = 'profile_load'
random_flag = True
Result = ReadCSV(folder_name, input_type) # read requests information from input csv file
requests = Result[0]
ideal_type_load = modify_ideal_load(Result[1][-1], unit)
initialResult = initialSituation(ideal_type_load, requests, unit, approach_name)
original_observed_load = initialResult[1] # get the observed load without random approach
original_loads = [original_observed_load]
random_observed_load = calcRandLoad(Result, unit, approach_name) # get observed load
## get output relative parameters
random_observed_loads = [random_observed_load]
write_output(ideal_type_load, original_loads, random_observed_loads, folder_name, input_type, random_flag) # output results
print 'Job Done.'
def writeYamlFileWithLargeNumOfHouses(folderName, inputType, unit, approachName, houseNumber):
"""
This method used to create a yaml file with the large number of houses. In order to save code time, several
methods will import from 'yamlCalcMethods' and 'yamlWriteMethods'. Consider of the tiny different requirement,
it is better to write a new python to construct yaml file.
str, str, int, str, int, float => file
"""
print "START read request type file"
RequestTypeDict = CsvToLoad.readExperiment(folderName)
for RequestType in RequestTypeDict.values():
load_list = CsvToLoad.extractApplianceProfile(RequestType, unit)
CsvToLoad.generateLoadTxt(load_list, RequestType.request_name, folderName, unit)
print "START read requests file"
Result = ReadCSV(folderName, inputType) # read requests information from input csv file
requests = list(Result[0])
houseLayout = yamlCalcMethods.getHouseLayout(houseNumber, requests)
requestNameMinMap = yamlCalcMethods.getRequestNameMinMap(requests) # get the requestNameMinMap
ideal_shiftable_load = modify_ideal_load(Result[1][-1], unit) # get ideal shift-able load
signal_load = list(ideal_shiftable_load) # make ideal shift-able load equal to signal load
requests = updateProfile(requests, unit) # update profile
requests = getProbDistribution(requests, signal_load, unit, approachName) # update probabilities
fileCount = 0
totalRequestNumber = getTotalRequestNumber(requests)
restRequestNumber = int(totalRequestNumber)
while restRequestNumber != 0:
if restRequestNumber < houseNumber:
repeatNumber = restRequestNumber
else:
repeatNumber = houseNumber
fileName = "".join(("CSVdata/", folderName, "/resident/" "resident_", folderName, "_", str(fileCount), ".yaml"))
applianceMap = getApplianceMap(repeatNumber, requests, unit) # get the start time and request name dict
requests = setNewRequests(requests) # delete the request which quantity is zero
writeFile = open(fileName, "w")
yamlWriteMethods.writeConstParameters(writeFile)
yamlWriteMethods.writeHouseLayout(writeFile, houseLayout)
yamlWriteMethods.writeVariableList(writeFile, houseLayout)
yamlWriteMethods.writeConstSimulation(writeFile)
yamlWriteMethods.writeConstNetwork(writeFile)
yamlWriteMethods.writeConstHeartbeat(writeFile)
yamlWriteMethods.writeTimeSerialLoop(writeFile, applianceMap, folderName) # write load / time_series content
yamlWriteMethods.writeConstBus(writeFile)
yamlWriteMethods.writeConstGenericgen(writeFile)
yamlWriteMethods.writeConstLoop(writeFile)
applianceCount = 0
yamlWriteMethods.writeAllAppliance(houseLayout, writeFile, applianceMap, applianceCount, requestNameMinMap)
writeFile.close()
print "No.",
print fileCount,
print "yaml file had constructed"
fileCount = fileCount + 1
restRequestNumber = restRequestNumber - repeatNumber
print "CONSTRUCT ",
print fileCount,
print "yaml files"
def calcRandLoad(Result, unit, approach_name):
"""
This method used to get the load information based on random number
for each request from each 'house'. Then get a load result.
[request,...], int, string => [float, float,...]
"""
ideal_shiftable_load = modify_ideal_load(Result[1][-1], unit) # get ideal shift-able load
signal_load = list(ideal_shiftable_load) # make ideal shift-able load equal to signal load
## first of all, get requests from reading result
requests = Result[0]
requests = updateProfile(requests, unit) # update profile
requests = getProbDistribution(requests, signal_load, unit, approach_name) # update probabilities
for request in requests:
#print 'before: '
#print request.request_type.request_name, ' : ', request.probabilities
getPureProbabilities(request) # get the new probabilities
quantitiy = request.quantity
newProbabilities = [0] * len(request.pure_probabilities)
for count in range(quantitiy):
randomNum = random.random()
if randomNum < request.pure_probabilities[0] or randomNum == request.pure_probabilities[0] :
newProbabilities[0] = newProbabilities[0] + 1
continue
elif randomNum > request.pure_probabilities[-1] or randomNum == request.pure_probabilities[-1]:
newProbabilities[-1] = newProbabilities[-1] + 1
continue
else:
for probCount in range(len(request.pure_probabilities)):
if randomNum > request.pure_probabilities[probCount]:
if randomNum < request.pure_probabilities[probCount + 1] or randomNum == request.pure_probabilities[probCount + 1]:
newProbabilities[probCount + 1] = newProbabilities[probCount + 1] + 1
break
else:
continue
for count in range(len(newProbabilities)):
newProbabilities[count] = newProbabilities[count] / (quantitiy * 1.0)
request.update_pure_probabilities(newProbabilities)
## then, modify the actual probabilities
modifyActualProbabilities(request)
print 'Finish Request: ', request.request_type.request_name
#print 'after: '
#print request.request_type.request_name, ' : ', request.probabilities
observed_load = calculateExpectedLoad(requests, unit)
return observed_load