Using the DynaSol Forecasting Model

$1.$ To answer parts $($a$)($c$)$ of this question, go to the Wind Turbines section of the Simple Moving Average $($SMA$)$ worksheet.

a$)$ Based on the values of BIAS, what can you conclude about the month $25$ forecast for wind turbines? Is the forecast likely to be too high, or will it be too low?

b$)$ Which value for n $($number of periods including in the moving average$)$ will result in the lowest MAD in month $24?$ Why? $($Hint: Look at the XY scatter chart for demand.$)$

c$)$ If the demand for wind turbines were as in the table shown below:

Now for which value for n will the MAD in month $24$ be the lowest? Why? $($Restore the original demand values before answering the other questions in this section.$)$

$2.$ To answer parts $($a$)$ and $($b$)$ of this question, go to the Solar Heating Equipment section of the Exponential Smoothing $($ES$)$ worksheet.

a$)$ Make the alpha smoothing parameter $(\backslash$alpha $)$ equal to $0\mathrm{.}5,$ and look at the BIAS column of the table. The numbers in this column form a repeating pattern: there are several negative numbers, followed by several positive numbers, followed again by negative numbers. What causes this alternating pattern? $($Hint: Look at the XY scatter chart for demand.$)$ Why do you suppose this pattern appears in the solar heating equipment data, but not the wind turbine data? $($Hint: When do customers buy heating equipment?$)$

b$)$ Try changing the alpha smoothing parameter in the Solar Heating Equipment section to the values $0\mathrm{.}1$ and $0\mathrm{.}9.$ Which value for $\backslash$alpha results in the lowest MAD in month $24?$ Why?

Again, restore the original demand values before answering the following questions.

$3.$ Go to the Wind Turbines section of the Exponential Smoothing $($ES$)$ worksheet. Try changing the alpha smoothing parameter to the values $0\mathrm{.}2,0\mathrm{.}5,$ and $0\mathrm{.}8.$ Which value for $\backslash$alpha results in the lowest MAD in month $24?$ Why?

Again, restore the original demand values before answering the following questions.

$4.$ To answer this question, go to the Wind Turbines section of the Linear Regression worksheet model that you have developed.

How does the best Linear Regression forecast for wind turbines you have developed compare with the best ES forecast for wind turbines from question $3?$ Compare the forecasting methods with respect to MAD, BIAS and MSE. Also interpret the value of the adjusted R$2.$ Which forecasting model performs better? Why?

$5.$ To answer this question, go to the Solar Heating Equipment section of the Linear Regression worksheet model that you have developed.

Again, interpret the value of the adjusted R$2?$ Is the forecast for month $27$ likely to be accurate? Why or why not? What is missing from the model and how would you fix it$?$

$6.$ Which forecasting method would you choose to use for the Wind Turbines data? Which would you choose for the Solar Heating Equipment data? Why?