Modeling of Long‐Term Load Forecast in Jordan Based on Statistical Techniques

The paper proposes a mathematical model for long‐term load forecast (LTLF) based on parametric and time series statistical techniques. The flowchart of the proposed algorithm was also presented. The multiple linear regression (MLR) as well as the autoregressive integrated moving average (ARIMA) model with different model orders are employed in load forecasting. Historical data from 1990 to 2022 were utilized in the model implementation and validation. The input data imply gross domestic product (GDP), oil prices, population, and the energy from renewable energy projects as independent variables, and the annual peak load is the dependent variable. The results obtained by MLR show that population and GDP have a positive impact on electricity demand, whereas the oil price and the penetration of the renewable energy have a negative impact on electricity demand. In ARIMA, the load forecast is estimated based on error (residual) estimation that is determined based on the time lag operator and autoregressive and moving average coefficients. The ARIMA ( p , d , and q ) model with six different model orders is investigated. The Bayesian information criteria (BIC) and Akaike information criteria (AIC) indices are used as a measure in the selection of the appropriate model for prediction. The comparison between the six model’s scenarios shows that ARIMA (1, 1, 1) is the best model that fits the time series with minimum error and with the lowest BIC and AIC. The RMSE, MAPE, MSE, and MAE provided by ARIMA (1, 1, 1) are 3.57%, 2.55%, 0.13%, and 54.36, respectively, whereas the AIC and BIC are 386.8 and 394.0, respectively. A comparison between ARIMA and MLR shows that ARIMA is better than MLR in terms of time series fitting and error level. The LTLF forecast period covering the period 2024–2035 considers three scenarios of future development in the country: the normal (medium forecast), the optimistic (high forecast), and the pessimistic (low forecast). The forecast is made in the three directions (i.e., medium, high, and low) to avoid the ambiguity and uncertainty that may occur in the data input used in forecasting the electricity demand. A comparison between the MLR and ARIMA in the last year 2035 shows a small deviation between the two methods with the forecasted values in the range between 5480 MW and 5520 MW, respectively.