SHORT-TERM ELECTRICITY LOAD FORECASTING SEASONAL PATTERN USING TIME SERIES REGRESSION (TSR) MODEL IN PT.PLN (PERSERO) MEDAN CITY - Feby Mayori Rambe; Rina Widyasari (Journal of Computer Networks, Architecture and High Performance Computing, 2025)
Electricity is a crucial component of modern life, where daily consumption fluctuates significantly. Uncertain electricity demand can lead to imbalances between supply and consumption, potentially causing energy wastage or power outages. To address this issue, a forecasting method capable of accurately predicting electricity load is essential. The Time Series Regression (TSR) model is applied for short-term electricity load forecasting by considering daily and weekly seasonal patterns. The forecasting results indicate that Monday and Tuesday have the highest electricity load, while Sunday has the lowest. When the Kolmogorov-Smirnov test is used to analyse the model, the p-value is 0.9608, which shows that the residuals have a normal distribution. The model's accuracy is assessed with a Root Mean Square Error (RMSE) value of 378.0069 MW, which is relatively high for a small dataset. Given the considerable forecasting error, further improvements such as hybrid models are recommended to enhance accuracy. The implementation of these forecasting results can help optimize electricity management and improve power distribution efficiency.