1 setwd("/Users/bp/Desktop/CONTRATS_AirNormand/2016/RapportFinalBruno")
5 pm = read.table("DATA/mesures_horaires_hloc_pm10_a_filer.csv",sep=";",dec=".",header=T) #,row.names=1)
7 datedebut = "10/12/2008"
9 # Chargement des données météo et indicateurs
10 VarExp <- read.table("DATA/meteo_extra_jourMois.csv",sep=";",dec=".",header=T)
14 dates = read.table("DATA/Dates_jours.csv",header=F,as.is=T)
17 # Contruction des matrices de données
18 pm.h <- matrix(pm[,2],ncol=24,byrow=TRUE)
21 Data = cbind(pm.h[1:(Nlignes -1), ], pm.h[2:Nlignes, ])
22 dates2 = dates[2:Nlignes]
23 rownames(Data) = dates2
24 # df contient l'ensemble des données.
25 #df <- cbind(Data,varexp[,-1])
27 PMjour <- apply(df[,25:48],1,mean,na.rm=T)
28 dfexp <- cbind(VarExp,PMjour)
35 Categorie = c("Epandage", "Chauffage", "Non Polluée")
37 RepFig = "FIGURES_Etude"
42 j=1 # numéro de semaine
43 ij=6 # numéro du jour (0 = lundi)
56 # Premier conditionnement : mois
57 indcond <- dfexp[,"Mois"] == 2 | dfexp[,"Mois"] == 3 | dfexp[,"Mois"] == 4 | dfexp[,"Mois"] == 9 | dfexp[,"Mois"] == 10
59 varexp = dfexp[indcond,]
61 nl = (1:nrow(data))[rownames(data)==Dates[j]]
62 dateJPrev = rownames(data)[nl+ij]
63 dataj = as.numeric(data[nl+ij, 1:48])
64 data = data[-(nl + ij), ]
65 varexp = varexp[-(nl + ij), ]
66 indNA = attr(na.omit(data[, 1:48]),"na.action")
68 varexp = varexp[-indNA,]
70 # Conditionnement : les jours avec PMjour +/- large
72 bornes = mean(dataj[25:48])+c(-large,large)
73 indcond = varexp[,"PMjour"]>=bornes[1] & varexp[,"PMjour"]<=bornes[2]
75 varexp = varexp[indcond,]
78 for (k in 1:nrow(data)) {
79 #D[k] = sqrt(sum((1:H)*(dataj[L] -data[k,L])^2))
80 D[k] = sqrt(sum((dataj[L] -data[k,L])^2))
82 ind = order(D)[1:nbvois]
86 JourMoy = apply(data[ind, 1:48], 2, mean)
87 #JourMoy = apply(W*data[ind, 1:48], 2, sum)
88 NomFile = paste("Voisins_Epandage_PMjour_Hc_",Hc,".png",sep="")
89 Titre = paste("Jour à prévoir : ",dateJPrev," - ", length(ind)," voisins",sep="")
90 #erreur = sqrt(sum((dataj[25:48] - JourMoy[25:48])^2))
91 if(Hc==24){erreurPrev=NA}else{erreurPrev = mean(abs(dataj[(H+1):48] - JourMoy[(H+1):48]))}
92 erreur24 = mean(abs(dataj[25:48] - JourMoy[25:48]))
94 matplot(t(data[ind, 1:48]), type = "l", lwd=1.4, lty=1, col=1:length(ind),
95 cex.axis=1.4, cex.main = 1.7, cex.lab=1.5,
96 xlab="Heures locales", ylab=paste0("PM10 - Erreurs = ",round(erreur24,1)," / ",round(erreurPrev,1)), main=Titre)
97 legend("top",rownames(data)[ind],ncol=2,lwd=1.4, lty=1, col=1:length(ind))
98 lines(1:48, dataj, lwd=2.5)
99 lines(JourMoy, lty = 2, lwd=2)
100 abline(v=c(24.5,H+0.5), lty = 2, lwd=1.2)
105 Err24 = c(Err24, erreur24)
106 ErrPrev = c(ErrPrev, erreurPrev)
107 ResDates = cbind(ResDates, rownames(data)[ind])
111 rownames(ResDates) = 1:10
114 for (Col in ncol(ResDates):1) {
118 if (ResDates[I,1] == ResDates[J,Col]) K = K +1
124 # pdf("Erreur_Epandage_PMjour.pdf")
125 ymin = min(na.omit(ErrPrev), Err24)
126 ymin = min(ymin, Kvois)
127 ymax = max(na.omit(ErrPrev), Err24)
128 ymax = max(ymax, Kvois)
129 plot(5:24,Err24, type = "l", lwd = 2, cex.axis=1.4, cex.lab = 1.5,
130 ylab = "MAE",xlab = "Heures de prévision", ylim= c(ymin,ymax))
131 lines(5:24,ErrPrev, lwd=2, lty = 2)
132 legend("topright", legend=c("Erreur 24h", "Erreur prévision"), lty = c(1,2), lwd=2, cex=1.5)
133 points(5:24, Kvois, cex=1.8, pch = 19)