  In this paper, we consider energy demand prediction in district heating
systems. Effective energy demand prediction is essential in combined heat power
systems when offering electrical energy in competitive electricity markets. To
address this problem, we propose two sets of algorithms: (1) a novel extension
to the algorithm proposed by E. Dotzauer and (2) an autoregressive predictor
based on hour-of-week adjusted linear regression on moving averages of energy
consumption. These two methods are compared against state-of-the-art artificial
neural networks. Energy demand predictor algorithms have various computational
costs and prediction quality. While prediction quality is a widely used measure
of predictor superiority, computational costs are less frequently analyzed and
their impact is not so extensively studied. When predictor algorithms are
constantly updated using new data, some computationally expensive forecasting
methods may become inapplicable. The computational costs can be split into
training and execution parts. The execution part is the cost paid when the
already trained algorithm is applied to predict something. In this paper, we
evaluate the above methods with respect to the quality and computational costs,
both in the training and in the execution. The comparison is conducted on a
real-world dataset from a district heating system in the northwest part of
Poland.
