--- /dev/null
+## File : 06_predictions-ICAME.r
+## Description : Prévoir les synchrones des classes ICAME.
+
+
+rm(list = ls())
+
+if(Sys.info()[4] == "mojarrita"){
+ setwd("~/Documents/projects/2014_EDF-Orsay-Lyon2/codes/")
+} else {
+ setwd("~/2014_EDF-Orsay-Lyon2/codes/")
+}
+
+## 1. Read auxiliar data files ####
+#identifiants <- read.table("identifs.txt")[ ,1]
+dates0 <- read.table("datesall.txt")[, 1]
+dates09 <- as.character(dates0[grep("2009", dates0)])
+dates10 <- as.character(dates0[grep("2010", dates0)])
+dates <- c(dates09, dates10)
+rm(dates0, dates09, dates10)
+
+#n <- length(identifiants)
+p <- length(dates)
+
+# groups for the predictor
+gr_calen <- read.table("calendar_transition_groups-1996-2011.txt")
+i_start_calen <- which(rownames(gr_calen) == "2009-01-01")
+i_end_calen <- which(rownames(gr_calen) == "2010-12-31")
+gr <- gr_calen$gr[i_start_calen:i_end_calen]
+gr <- gsub("Thu", "TWT", gr)
+gr <- gsub("Wed", "TWT", gr)
+gr <- gsub("Tue", "TWT", gr)
+
+days <- seq.Date(from = as.Date("2009/01/01"),
+ to = as.Date("2010/12/31"),
+ by = 'day')
+
+
+setwd("../classes ICAME/synchrones_classif_ICAME/")
+files_ICAME <- dir(pattern = "*TR-32V*")
+K_ICAME <- length(files_ICAME)
+SC <- matrix(NA_real_, nrow = K_ICAME, ncol = p + 4) # NA missing in dates
+ # (summer time saving)
+
+for(k in 1:K_ICAME){
+ df <- read.table(file = files_ICAME[k], header = TRUE)
+ SC[k, ] <- subset(x = df, # series must be on rows
+ subset = Year == "2009" | Year == "2010",
+ select = "Load")[[1]]
+}
+
+
+rm(df, files_ICAME, k)
+rm(gr_calen, i_start_calen, i_end_calen)
+
+# Set start and end timepoints for the prediction
+i_00 <- which(days == "2009-01-01")
+n_00 <- which(days == "2009-12-31")
+n_01 <- which(days == "2010-12-31")
+
+
+## 2. Transform data into wkdata ####
+library(kerwavfun)
+
+mat_Load <- matrix(colSums(SC), ncol = 48, byrow = TRUE)
+mat_ks <- lapply(1:K_ICAME, function(k) matrix(data = SC[k, ],
+ ncol = 48,
+ byrow = TRUE))
+
+wk_Load <- wkdata(X = c(t(mat_Load)),
+ gr = gr[1:nrow(mat_Load)],
+ p = 48)
+
+wks <- lapply(mat_ks, function(mat_k)
+ wkdata(X = c(t(mat_k)), gr = gr[1:nrow(mat_Load)], p = 48))
+
+## 3. Begin of big loop over grid ##
+grid <- n_00:(n_01 - 1) # these are the reference days
+#h_Load <- h_k1 <- h_k2 <- h_k3 <- double(length(grid))
+
+# output matrix of predicted days
+# names of the predicted days (reference days + 1)
+pred_Load <- matrix(NA_real_, ncol = 48, nrow = length(grid))
+preds <- lapply(1:K_ICAME, function(k) pred_Load)
+
+go <- Sys.time()
+for(q in seq_along(grid)[-c(121, 122, 227, 228)]) { #seq_along(grid)[-c(46:47)]
+ if((q %/% 10) == (q / 10)) cat(paste(" iteration: ", q, "\n"))
+ Q <- grid[q]
+
+ select_Load <- select.wkdata(wk_Load, i_00:Q)
+ aux_Load <- wavkerfun(obj = select_Load, kerneltype = "gaussian",
+ EPS = .Machine$double.eps)
+ pred_Load[q, ] <- predict.wavkerfun(obj = aux_Load, cent = "DIFF")$X
+
+ selects <- lapply(wks, function(k) select.wkdata(k, i_00:Q))
+ auxs <- lapply(selects,
+ function(k) wavkerfun(obj = k,
+ kerneltype = "gaussian",
+ EPS = .Machine$double.eps))
+ for(k in 1:K_ICAME)
+ preds[[k]][q, ] <- predict.wavkerfun(obj = auxs[[k]], cent = "DIFF")$X
+
+}
+top <- Sys.time()
+#rm(wks, selects, auxs, aux_Load, mat_ks, q, Q, wk_Load)
+
+
+preds <- lapply(preds, function(dd) {dd[dd<0] <- 0; dd} )
+# Compute the prediction of synchrone by the sum of individuals
+pred_Load_sum <- Reduce('+', preds)
+
+
+vect_preds <- data.frame(lapply(preds, function(dd) c(t(dd))))
+names(vect_preds) <- paste0("icame", 1:K_ICAME)
+
+vect_Load <- c(t(pred_Load))
+vect_gtop <- matrix(NA_real_, ncol = K_ICAME, nrow = length(vect_Load))
+
+
+for(tt in seq_along(vect_gtop)) {
+ VALID <- !any(is.na(vect_preds[tt, ])) & # compute gtop only when no NAs
+ tt < 17511 # these rows present a problem ==> why? (matbe too much 0s in preds_indiv)
+ if(VALID) {
+ res <- gtop(preds_indiv = as.numeric(vect_preds[tt, ]),
+ pred_total = vect_Load[tt],
+ weights_indiv = rep(1, K_ICAME),
+ weight_total = 1,
+ bounds_indiv = 0.05 * as.numeric(vect_preds[tt, ]))
+ vect_gtop[tt, ] <- res$preds_indiv[1:K_ICAME] # last value is the sum
+ }
+ }
+
+vect_gtop_sum <- rowSums(vect_gtop)
+pred_gtop_sum <- matrix(vect_gtop_sum, ncol = 48, byrow = TRUE)
+
+## 4. Analisis of the predictions ####
+resids_Load <- pred_Load - mat_Load[grid + 1, ] # i.e. reference days + 1
+resids_Load_sum <- pred_Load_sum - mat_Load[grid + 1, ] # i.e. reference days + 1
+resids_gtop_sum <- pred_gtop_sum - mat_Load[grid + 1, ]
+
+mape_Load <- 100 * rowMeans(abs(resids_Load / mat_Load[grid + 1, ]))
+rmse_Load <- sqrt(rowMeans(resids_Load^2))
+
+mape_Load_sum <- 100 * rowMeans(abs(resids_Load_sum / mat_Load[grid + 1, ]))
+rmse_Load_sum <- sqrt(rowMeans(resids_Load_sum^2))
+
+mape_gtop_sum <- 100 * rowMeans(abs(resids_gtop_sum / mat_Load[grid + 1, ]))
+rmse_gtop_sum <- sqrt(rowMeans(resids_gtop_sum^2))
+
+
+mean(mape_Load, na.rm = TRUE)
+mean(mape_Load_sum, na.rm = TRUE)
+mean(mape_gtop_sum, na.rm = TRUE)
+#print(mean(mape_Load, na.rm = TRUE))
+#print(mean(mape_Load_sum, na.rm = TRUE))
+
+
+mean(rmse_Load, na.rm = TRUE)
+mean(rmse_Load_sum, na.rm = TRUE)
+mean(rmse_gtop_sum, na.rm = TRUE)
projects / epclust.git / blobdiff