Electriciteitsvraag voorspellen

Je kunt de dagelijkse electriciteitsvraag ook modelleren als een functie van temperatuur. Zoals je misschien op je energierekening hebt gezien, wordt er op hete dagen meer elektriciteit verbruikt door airconditioning en op koude dagen door verwarming.

In deze oefening pas je een kwadratisch regressiemodel met een ARMA-fout toe. Eén jaar aan dagelijkse data staat in elecdaily, met daarin de totale dagelijkse vraag, een indicatorvariabele voor werkdagen (een werkdag is 1, een niet-werkdag is 0), en de dagelijkse maximumtemperaturen. Omdat er wekelijkse seizoenspatronen zijn, is de frequency ingesteld op 7.

Laten we de eerste drie rijen bekijken:

> elecdaily[1:3, ]
       Demand Temperature Workday
[1,] 174.8963        26.0       0
[2,] 188.5909        23.0       1
[3,] 188.9169        22.2       1

elecdaily is alvast in je werkruimte geladen.

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Maak tijdplots van alleen de dagelijkse vraag en de maximumtemperaturen met facetten.
Stel een matrix met regressoren op met MaxTemp voor de maximumtemperaturen, MaxTempSq voor het kwadraat van de maximumtemperatuur, en Workday, in die volgorde. Duidelijk is dat het tweede argument van cbind() een eenvoudige wiskundige bewerking vereist.
Pas een dynamisch regressiemodel toe op de kolom met vraag met ARIMA-fouten en noem dit fit.
Als de volgende dag een werkdag is (indicator is 1) met een verwachte maximumtemperatuur van 20°C, wat is dan de voorspelde vraag? Vul de juiste waarden in cbind() in voor het xreg-argument in forecast().

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Time plots of demand and temperatures
autoplot(elecdaily[, c(___, ___)], facets = ___)

# Matrix of regressors
xreg <- cbind(MaxTemp = elecdaily[, "Temperature"],
              MaxTempSq = ___,
              Workday = ___)

# Fit model
fit <- auto.arima(___, xreg = xreg)

# Forecast fit one day ahead
forecast(___, xreg = cbind(___, ___, ___))

Code bewerken en uitvoeren

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The first thing to do in any data analysis task is to plot the data. Graphs enable many features of the data to be visualized, including patterns, unusual observations, and changes over time. The features that are seen in plots of the data must then be incorporated, as far as possible, into the forecasting methods to be used.

Exercise 1: Welcome to Forecasting Using R Exercise 2: Creating time series objects in R Exercise 3: Time series plots Exercise 4: Seasonal plots Exercise 5: Trends, seasonality, and cyclicity Exercise 6: Autocorrelation of non-seasonal time series Exercise 7: Autocorrelation of seasonal and cyclic time series Exercise 8: Match the ACF to the time series Exercise 9: White noise Exercise 10: Stock prices and white noise

In this chapter, you will learn general tools that are useful for many different forecasting situations. It will describe some methods for benchmark forecasting, methods for checking whether a forecasting method has adequately utilized the available information, and methods for measuring forecast accuracy. Each of the tools discussed in this chapter will be used repeatedly in subsequent chapters as you develop and explore a range of forecasting methods.

Exercise 1: Forecasts and potential futures Exercise 2: Naive forecasting methods Exercise 3: Fitted values and residuals Exercise 4: Checking time series residuals Exercise 5: Training and test sets Exercise 6: Evaluating forecast accuracy of non-seasonal methods Exercise 7: Evaluating forecast accuracy of seasonal methods Exercise 8: Do I have a good forecasting model?Exercise 9: Time series cross-validation Exercise 10: Using tsCV() for time series cross-validation

Forecasts produced using exponential smoothing methods are weighted averages of past observations, with the weights decaying exponentially as the observations get older. In other words, the more recent the observation, the higher the associated weight. This framework generates reliable forecasts quickly and for a wide range of time series, which is a great advantage and of major importance to applications in business.

Exercise 1: Exponentially weighted forecasts Exercise 2: Simple exponential smoothing Exercise 3: SES vs naive Exercise 4: Exponential smoothing methods with trend Exercise 5: Holt's trend methods Exercise 6: Exponential smoothing methods with trend and seasonality Exercise 7: Holt-Winters with monthly data Exercise 8: Holt-Winters method with daily data Exercise 9: State space models for exponential smoothing Exercise 10: Automatic forecasting with exponential smoothing Exercise 11: ETS vs seasonal naive Exercise 12: Match the models to the time series Exercise 13: When does ETS fail?

ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.

Exercise 1: Transformations for variance stabilization Exercise 2: Box-Cox transformations for time series Exercise 3: Non-seasonal differencing for stationarity Exercise 4: Seasonal differencing for stationarity Exercise 5: ARIMA models Exercise 6: Automatic ARIMA models for non-seasonal time series Exercise 7: Forecasting with ARIMA models Exercise 8: Comparing auto.arima() and ets() on non-seasonal data Exercise 9: Seasonal ARIMA models Exercise 10: Automatic ARIMA models for seasonal time series Exercise 11: Exploring auto.arima() options Exercise 12: Comparing auto.arima() and ets() on seasonal data

The time series models in the previous chapters work well for many time series, but they are often not good for weekly or hourly data, and they do not allow for the inclusion of other information such as the effects of holidays, competitor activity, changes in the law, etc. In this chapter, you will look at some methods that handle more complicated seasonality, and you consider how to extend ARIMA models in order to allow other information to be included in the them.

Exercise 1: Dynamische regressie Exercise 2: Verkopen voorspellen met advertentiebudget als verklarende variabele Exercise 3: Electriciteitsvraag voorspellen

Huidige oefening

Exercise 4: Dynamische harmonische regressie Exercise 5: Wekelijkse data voorspellen Exercise 6: Harmonische regressie voor meerdere seizoenspatronen Exercise 7: Reserveringen voor telefoontjes voorspellen Exercise 8: TBATS-modellen Exercise 9: TBATS-modellen voor elektriciteitsvraag Exercise 10: Jouw toekomst in forecasting!