Forecasting Public Electricity Consumption with ARIMA Model: A Case Study from Italian Municipalities Energy Data

Electricity consumption forecasting is an important part in the energy monitoring sector. In the case of private electricity the forecasting analysis depends of several demand especially in public sectors. For this purpose kindly prediction methods are used. In this study autoregressive integrated moving average (ARIMA) method based on the idea to remove cycling components in time series. For removing cycling, time series divided monthly data and merged co-exhibiting behaviour months. Same months and different years data is merged and called as "Model" and 6 Models are prepared. Last model; Model 6 is a general model that includes all consumption data. ARIMA models are applied and mean absolute percent errors (MAPE) are found. Selected minimum MAPE and values of (p,d,q) predictions for Models. For 2018, predictive values of models and Model 6 are compared with actual consumptions. Model that removed cycling (Merged Model) 2.3% better than Model 6.