| 1 | setwd("/Users/bp/Desktop/CONTRATS_AirNormand/2016/RapportFinalBruno") |
| 2 | rm(list=ls()) |
| 3 | |
| 4 | # Lecture des données |
| 5 | pm = read.table("DATA/mesures_horaires_hloc_pm10_a_filer.csv",sep=";",dec=".",header=T) #,row.names=1) |
| 6 | n = dim(pm)[1] |
| 7 | datedebut = "10/12/2008" |
| 8 | |
| 9 | # Chargement des données météo et indicateurs |
| 10 | VarExp <- read.table("DATA/meteo_extra_jourMois.csv",sep=";",dec=".",header=T) |
| 11 | VarExp <- VarExp[-1,] |
| 12 | |
| 13 | # Lecture des dates |
| 14 | dates = read.table("DATA/Dates_jours.csv",header=F,as.is=T) |
| 15 | dates = dates[,1] |
| 16 | |
| 17 | # Contruction des matrices de données |
| 18 | pm.h <- matrix(pm[,2],ncol=24,byrow=TRUE) |
| 19 | |
| 20 | Nlignes = nrow(pm.h) |
| 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]) |
| 26 | df <- Data |
| 27 | PMjour <- apply(df[,25:48],1,mean,na.rm=T) |
| 28 | dfexp <- cbind(VarExp,PMjour) |
| 29 | |
| 30 | Dates = c( |
| 31 | "16/03/2015", |
| 32 | "19/01/2015", |
| 33 | "27/04/2015") |
| 34 | |
| 35 | Categorie = c("Epandage", "Chauffage", "Non Polluée") |
| 36 | |
| 37 | RepFig = "FIGURES_Etude" |
| 38 | |
| 39 | ResDates = NULL |
| 40 | |
| 41 | nbvois=10 |
| 42 | j=1 # numéro de semaine |
| 43 | ij=6 # numéro du jour (0 = lundi) |
| 44 | |
| 45 | Err24 = NULL |
| 46 | ErrPrev = NULL |
| 47 | Kvois = NULL |
| 48 | |
| 49 | for (Hc in 5:24) { |
| 50 | |
| 51 | H=24+Hc |
| 52 | fen=H |
| 53 | #fen=12 ou 24 ou H |
| 54 | L = (H-fen+1):H |
| 55 | |
| 56 | # Premier conditionnement : mois |
| 57 | indcond <- dfexp[,"Mois"] == 2 | dfexp[,"Mois"] == 3 | dfexp[,"Mois"] == 4 | dfexp[,"Mois"] == 9 | dfexp[,"Mois"] == 10 |
| 58 | data = df[indcond,] |
| 59 | varexp = dfexp[indcond,] |
| 60 | |
| 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") |
| 67 | data = data[-indNA,] |
| 68 | varexp = varexp[-indNA,] |
| 69 | |
| 70 | # Conditionnement : les jours avec PMjour +/- large |
| 71 | large = 1 |
| 72 | bornes = mean(dataj[25:48])+c(-large,large) |
| 73 | indcond = varexp[,"PMjour"]>=bornes[1] & varexp[,"PMjour"]<=bornes[2] |
| 74 | data = data[indcond,] |
| 75 | varexp = varexp[indcond,] |
| 76 | |
| 77 | D = rep(0,nrow(data)) |
| 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)) |
| 81 | } |
| 82 | ind = order(D)[1:nbvois] |
| 83 | w = 1/(D[ind]^2) |
| 84 | w = w/sum(w) |
| 85 | W = w%o%rep(1,48) |
| 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])) |
| 93 | png(NomFile) |
| 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) |
| 101 | xx=dev.off() |
| 102 | |
| 103 | |
| 104 | |
| 105 | Err24 = c(Err24, erreur24) |
| 106 | ErrPrev = c(ErrPrev, erreurPrev) |
| 107 | ResDates = cbind(ResDates, rownames(data)[ind]) |
| 108 | } |
| 109 | |
| 110 | |
| 111 | rownames(ResDates) = 1:10 |
| 112 | |
| 113 | Kvois = NULL |
| 114 | for (Col in ncol(ResDates):1) { |
| 115 | K = 0 |
| 116 | for (I in 1:10){ |
| 117 | for (J in 1:10){ |
| 118 | if (ResDates[I,1] == ResDates[J,Col]) K = K +1 |
| 119 | } |
| 120 | } |
| 121 | Kvois = c(Kvois, K) |
| 122 | } |
| 123 | |
| 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) |
| 134 | # xx = dev.off() |
| 135 | |
| 136 | length(D) |