oRaklE (version 1.0.2)

full_forecast: Title

Description

Title

Usage

full_forecast(
  start_year,
  end_year_data,
  country,
  test_set_steps = 2,
  future = "yes",
  end_year = 2028,
  data_directory = tempdir(),
  verbose = FALSE
)

Value

Returns a list with the combined model results and plots. And a list with the results, models and plots for the long-term trend, mid-term seasonality, and short-term seasonality respectively. The combined model predictions and plots are saved in the respective folder for the country.

full_model_predictions

The combined model results and plots.

longterm

The long-term trend models, results, and plots.

midterm

The mid-term seasonality models, results, and plots.

shortterm

The short-term seasonality models, results, and plots.

Arguments

start_year

Specifies the starting year for which predictions and models will be generated.

end_year_data

Specifies the final year for which data from the Transparency Platform of the European Network of Transmission System Operators for Electricity (ENTSO-E, https://transparency.entsoe.eu/) is retrieved and for which models will be generated.

country

String. Specifies the country.

test_set_steps

Integer. Specifies how many years are used for generating the test/validation set for the model selection.

future

String. Option to enable or disable the future forecasts. If set to "yes" forecasts will be made until the specified end_year. If set to anything else, forecasts will only be generated until the specified end_year_data value.

end_year

Specifies the final year for which future predictions will be generated.

data_directory

The path to the directory where the results and plots will be saved. The default is set to a temporary directory. It is highly recommended to set it to something that is not a temporary directory if you want to use this function.

verbose

A boolean value indicating if you want the generated plots and detailed status updates to be shown (set to TRUE if yes). #' @seealso See also functions long_term_lm, mid_term_lm, and short_term_lm for the prediction models and long_term_future, mid_term_future, and short_term_future for the future prediction models.

Examples

library(ggplot2)
## Without future predictions
# \donttest{
forecast_data <- full_forecast(
  start_year = 2017, end_year_data = 2021, country = "France", test_set_steps = 2,
  future = "no"
)
# }
ggplot(example_full_model_predictions) +
  geom_line(aes(date, hourly_demand, color = "actual")) +
  geom_line(aes(date, complete_model, color = "fitted")) +
  xlab("\nYear") +
  ylab("Hourly Demand\n [MW]\n") +
  geom_vline(xintercept = example_full_model_predictions$date[26280], linetype = 2) +
  ggthemes::theme_foundation(base_size = 14, base_family = "sans") +
  xlab("\nHour") +
  ylab("Hourly Demand\n [MW]\n") +
  ggtitle(paste("Complete Model Results - FR\n")) +
  theme(
    plot.title = element_text(
      face = "bold",
      size = rel(1.2), hjust = 0.5
    ),
    text = element_text(),
    panel.background = element_rect(colour = NA),
    plot.background = element_rect(colour = NA),
    panel.border = element_rect(colour = NA),
    axis.title = element_text(face = "bold", size = rel(1)),
    axis.title.y = element_text(angle = 90, vjust = 2),
    axis.title.x = element_text(vjust = -0.2),
    axis.text = element_text(),
    axis.line.x = element_line(colour = "black"),
    axis.line.y = element_line(colour = "black"),
    axis.ticks = element_line(),
    panel.grid.major = element_line(colour = "#f0f0f0"),
    panel.grid.minor = element_blank(),
    legend.key = element_rect(colour = NA),
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.key.size = unit(0.2, "cm"),
    plot.margin = unit(c(10, 5, 5, 5), "mm"),
    strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
    strip.text = element_text(face = "bold")
  ) +
  theme(legend.title = element_blank())

## With future predictions
# \donttest{
forecast_data <- full_forecast(
  start_year = 2017, end_year_data = 2021, country = "France", test_set_steps = 2,
  future = "yes", end_year = 2028
)
# }
suppressWarnings(
  ggplot(example_full_model_future_predictions) +
    geom_line(aes(1:nrow(example_full_model_future_predictions),
      hourly_demand,
      color = "actual"
    )) +
    geom_line(aes(1:nrow(example_full_model_future_predictions), complete_model,
      color = "fitted"
    )) +
    xlab("\nYear") +
    ylab("Hourly Demand\n [MW]\n") +
    geom_vline(xintercept = 26280, linetype = 2) +
    geom_vline(xintercept = 43800, linetype = 3) +
    ggthemes::theme_foundation(base_size = 14, base_family = "sans") +
    xlab("\nHour") +
    ylab("Hourly Demand\n [MW]\n") +
    scale_y_continuous(labels = scales::label_number(scalar = 1)) +
    ggtitle(paste("Complete Model Results - FR\n")) +
    theme(
      plot.title = element_text(
        face = "bold",
        size = rel(1.2), hjust = 0.5
      ),
      text = element_text(),
      panel.background = element_rect(colour = NA),
      plot.background = element_rect(colour = NA),
      panel.border = element_rect(colour = NA),
      axis.title = element_text(face = "bold", size = rel(1)),
      axis.title.y = element_text(angle = 90, vjust = 2),
      axis.title.x = element_text(vjust = -0.2),
      axis.text = element_text(),
      axis.line.x = element_line(colour = "black"),
      axis.line.y = element_line(colour = "black"),
      axis.ticks = element_line(),
      panel.grid.major = element_line(colour = "#f0f0f0"),
      panel.grid.minor = element_blank(),
      legend.key = element_rect(colour = NA),
      legend.position = "bottom",
      legend.direction = "horizontal",
      legend.key.size = unit(0.2, "cm"),
      plot.margin = unit(c(10, 5, 5, 5), "mm"),
      strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
      strip.text = element_text(face = "bold")
    ) +
    theme(legend.title = element_blank()) +
    scale_x_continuous(
      breaks = c(1, 8761, 17521, 26281, 35041, 43801, 52561, 61321, 70081, 78841, 87601, 96361),
      labels = c(2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028)
    ) +
    annotate("text", x = 13140, y = 99216.6, label = "Training", size = 4, hjust = 0.5, vjust = 0) +
    annotate("text", x = 35040, y = 99216.6, label = "Test", size = 4, hjust = 0.5, vjust = 0) +
    annotate("text", x = 74460, y = 99216.6, label = "Unknown", size = 4, hjust = 0.5, vjust = 0)
)