English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we consider energy demand prediction in district heatingsystems. Effective energy demand prediction is essential in combined heat powersystems when offering electrical energy in competitive electricity markets. Toaddress this problem, we propose two sets of algorithms: (1) a novel extensionto the algorithm proposed by E. Dotzauer and (2) an autoregressive predictorbased on hour-of-week adjusted linear regression on moving averages of energyconsumption. These two methods are compared against state-of-the-art artificialneural networks. Energy demand predictor algorithms have various computationalcosts and prediction quality. While prediction quality is a widely used measureof predictor superiority, computational costs are less frequently analyzed andtheir impact is not so extensively studied. When predictor algorithms areconstantly updated using new data, some computationally expensive forecastingmethods may become inapplicable. The computational costs can be split intotraining and execution parts. The execution part is the cost paid when thealready trained algorithm is applied to predict something. In this paper, weevaluate the above methods with respect to the quality and computational costs,both in the training and in the execution. The comparison is conducted on areal-world dataset from a district heating system in the northwest part ofPoland.

Quality versus speed in energy demand prediction for district heating systems