Day-ahead heat load forecasting during the off-season in the district heating system using Generalized Additive model

Abstract

The paper presents the results of heat demand forecasting during the off-season (summertime and transitional period) in a real district heating system. The hourly heat demand in the analyzed system ranges from 16 to 28 MW, depending on the month and hour of the day. Short-term prediction of the heat demand in the day-ahead horizon is needed for effective operation of production units in the Cogeneration Heat and Power Plant (CHP). The method of an hourly heat load predictions using a data-driven model is presented. The algorithm is based on the Generalized Additive Model with hour of the day, day of the week and day of the year as predictors. A way of increasing the accuracy of forecasts using the autoregressive model for residuals of the model was discussed. The influence of the training dataset size on the model error was also examined. The several versions of the model were evaluated by comparing forecasted results with real values during the district heating system's operation from June to the end of August. For the most accurate one, Root Mean Square and Mean Average Percentage Error were 0.84 MW and 3.3 %, respectively.