| Summary: | 碩士 === 真理大學 === 企業管理學系碩士班 === 101 === Taiwan is suitable to develop solar energy due to sufficient sun
exposure, high temperature, and located in subtropical regions. Solar
energy demand will be a potential orientation to study due to high cost
and restriction on emission of greenhouse gases. Therefore, this study
investigates solar energy output by coal, coal related product, crude,
crude related product, gas, hydroelectric and nuclear power and construct
the forecasting model to improve the prediction accuracy.
Combing Neural network with grey system model GM (1,1) to
establish the NNGM(1,1) forecast model in this study. The NNGM(1,1)
model is compared to traditional ARIMA and regression model the
NNGM(1,1) model in compared to. At first, using GM(1,1) model to
forecaste the solar energy output, the mean absolute percentage error
(MAPE) is up to 82.85%, it means the forecast is bad. Therefore,
choosing four related factors to construct GM(1,4) model and improve
MAPE to 4.04%. Then, only using neural network to establish the
forecasting model, the MAPE is 2.47%.
As the results, using the combination of neural network and grey
forecast model to propose NNGM(1,4) model, which lower MAPE to
1.76%.
Comparing to the traditional forecast models in this study, the
traditional ARIMA forecast model can get the high prediction accuracy
according to the sufficient history data.
|
|---|
