Seite - 242 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 1893 for larger sizes (tensofmillions). Ofcourseall theseconsiderationsdependheavilyon thespecific materialandtechnology.Werecall thatour interest isonrelativelystandardscientificworkstations. Thealgorithmweuseonthefirst stepof theclustering isdescribedbelow.Wethenshowtheresultsof theprofilingofourwholestrategytohighlightwhereare thebottleneckswhenonewishes toup-scale themethod.Weendthissectiondiscussingthesolutionsweproposed. 6.1.AlgorithmDescription Themassivedatasetclusteringalgorithmisas follows: 1. Dataserialization.Timeseriesaregiven inaverboseby-columnformat.Were-codeallof themin abinaryfile (if suitable),oradatabase. 2. Dimensionality reduction.Eachseriesof lengthN is replacedbythe log2(N)energeticcoefficients definedusingawaveletbasis. Eventuallya featureselectionstepcanbeperformedto further reductiononthenumberof features. 3. Chunking.Data ischunkedintogroupsofsizeatmostnc,wherenc isauserparameter (weuse nc=5000 in thenextsectionexperiments). 4. Clustering.Withineachgroup, thePAMclusteringalgorithmisruntoobtainK0 clusters. 5. Gathering. Afinal runofPAMisperformed toobtainK′mediods,K′ noutof thenc×K0 mediodsobtainedonthechunks.. FromtheseK′medoids thesynchronecurvesarecomputed(i.e., thesumofall curveswithineach groupforeachtimestep),andgivenonoutput for thepredictionstep. 6.2. CodeProfiling Figure 9 gives some timings obtainedbyprofiling the runs of our initial (C) code. Togive a clearer insight,wealsoreport thesizeof theobjectswedealwith. Thestartingpoint is theensembleof individual recordsofelectricitydemandforawholeyear.Here,wetreatover25,000clientssampled half-hourlyduringayear. The tabulationof thesedata toobtainamatrix representationsuitable tofit inmemorytakeabout7min. andrequiresover30Gbofmemory. Task Time Memory Disk Raw(15Gb) tomatrix 7min 30Gb 2.7Gb Computecontributions 7min <1Gb 7Mb 1ststageclustering 3min <1Gb – Aggregation 1min 6Gb 30Mb Werdistancematrix 40min 64Gb 150Kb Forecasts 10min <1Gb – Figure9. Codeprofilingbytasks. 6.3. ProposedSolutions Twomainsolutionsare tobediscussed, concerningthe internaldatastoragestrategyandtheuse ofasimpleparallelizationscheme. Theformer looks for reducingthecommunicationtimeof internal operationsusing serialization. The latter attacks themajor bottleneck of our clustering approach, that is theconstructionof theWERdissimilaritymatrix. The initial format (verbose, by-column) is clearly inappropriate for efficient data processing. Thereareseveraloptionsstartingfromthisdata format, they implyhavingall seriesstoredas • anASCIIfile,onesampleper line;very fast,butdataretrievalwilldependonlinenumber; • abinaryformat (3or4octetspervalue); compression isunadvisedsince itwould increaseboth preprocessingtimeand(bya largeamount) readingtimes; 242