The results indicate that the SARIMA (seasonal autoregressive integrated moving average) model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%.
Sonal Gupta, School of Business, UPES, Dehradun, India; School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh, UK.
Deepankar Chakrabarty, Jaipuria Institute of Management, Noida, India.
Rupesh Kumar, Associate Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
This research investigates the short-term (ST) forecasting performance of the daily prices of the Indian exchange-traded day-ahead (DAM) market, divided into 13 bid areas, each consisting of states with varied fundamentals. Forecasts are built employing SARIMA (seasonal autoregressive integrated moving average) and MLP (multilayer perceptron) methods. Moreover, the robustness and performance of the model is compared using the lowest error and the Diebold–Mariano (DM) test statistic values.
The results indicate that the SARIMA model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%. However, validation by the DM test suggests no statistical significant difference between the two models. The power generators, distribution companies, traders, policymakers, strategists and managers could use the findings for effective power management through proper planning.
Published in: OPEC Energy Review
To read the full article, please click here.