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