Abstract
The paper presents a developed methodology of short-term forecasting for heat production in combined heat and power (CHP) plants using a big data-driven model. An accurate prediction of an hourly heat load in the day-ahead horizon allows a better planning and optimization of energy and heat production by cogeneration units. The method of training and testing the predictive model with the use of generalized additive model (GAM) was developed and presented. The weather data as an input variables of the model were discussed to show the impact of weather conditions on the quality of predicted heat load. The new approach focuses on an application of the moving window with the learning data set from the last several days in order to adaptively train the model. The influence of the training window size on the accuracy of forecasts was evaluated. Different versions of the model, depending on the set of input variables and GAM parameters were compared. The results presented in the paper were obtained using a data coming from the real district heating system of a European city. The accuracy of the methods during the different periods of heating season was performed by comparing heat demand forecasts with actual values, coming from a measuring system located in the case study CHP plant. As a result, a model with an averaged percentage error for the analyzed period (November–March) of less than 7% was obtained.
Highlights
District heating systems (DHS) are common forms of heat distribution in large urban areas
This paper focuses on the use of the generalized additive model (GAM) method to develop the heat demand model in a medium-sized heating system supplied from a combined heat and power (CHP)
The presented state refers to the period when the maximum value of produced heat in the CHP is delivered to the district heating network (DHN)
Summary
District heating systems (DHS) are common forms of heat distribution in large urban areas. An accurate prediction of an hourly heat load in the day-ahead horizon allows better planning and optimization of heat and electricity production by cogeneration units. This paper focuses on the use of the generalized additive model (GAM) method to develop the heat demand model in a medium-sized heating system supplied from a CHP plant. In the GAM method, the forecast variable is estimated by smoothing the input variables with functional relationships [23] It is useful extension of the generalized linear model (GLM), able to effectively map the seasonality and non-linearity which is normally presented in the heat load data. The GAM method was applied to build an hourly heat demand model based on the weather data as ambient temperature, solar irradiation and wind speed. Particular attention was paid to parametrization and calibration of the model in order to obtain high accuracy in the day-ahead time horizon
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