新西兰代写-南方能源的电力需求预测

新西兰代写-南方能源的电力需求预测。南方能源(Southern Energy)正处于决定是否新建一座燃气发电厂的过程中。这座发电厂将专门为伦敦附近一个工业园区的一大制造业集群服务。未来5年的发电成本通过将生产的电力卖给工业园区的企业而产生的收入，发电成本因变量，天然气价格(需预估未来天然气价格)天然气价格(每立方米- m3)。替代燃料价格(PF，一种衡量其他可发电燃料(主要是石油和煤炭)价格的综合指数。全球经济环境的总体变化，以全球国内生产总值(GDP)的变化来衡量电力需求预测等。

In base scenario, it has been assumed that the conditions which drive the price of alternative fuel and trend for electricity demand will persist. The GDP change measure has been taken 1.5% per quarter.
Conditions that drive prices for alternative fuels and the trends of electricity demand in the industrial park will persist in the near future.
Southern Energy also assumes that global GDP change measure will be 1.5% per quarter
The multiple R for Gas demand is 0.4113 while the Multiple R for electricity price is 0.60864809. The multiple R states that gas price is tracking more closely than the electricity demand (Cohen, 1988).
The r square means coefficient of determination, for G it is 26% while for E it is 37%. This indicates that 26% o and 37% movements of both G&E is due to demand.
The adjusted r square will be always lower than r square as it is adjusted for the error of taking estimated price
The standard error in single index model is the SD of the residual value.
Comment –
Df stands for degree of freedom and the result is used for t-tests and f-test
Sum square and the Mean square are also used for the same tests (Kroeger & Weber, 2014).
The F valus is to determine the significance of regression analysis (Jennings, 1997).
Coefficient is intercepting the value of alpha using single index model. The P value indicate that there is 12 % chance n case of alpha and 67% chance in case of E to obtain the coefficient an conclude value. Therefore, we conclude that chances of obtaining co- efficient value are more for E than G (Demidenko, 2008).
The x variable indicates beta. The probability of getting this value of beta is more for E than G (Demidenko, 2008).
For base scenario, forecast has been calculated by multiple regression analysis.
In base scenario, the demand has been forecasted by using multiple regression models. The same model has been implemented for faster and slower growth scenario thereon. The faster growth rate scenario assumed that the global demand will increases and it would lead to higher price of alternative fuel and GDP rate. The slower growth rate predict lower price and GDP growth rate.

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