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Statistical Investigation of Climate Change Effects on the Utilization of the Sediment Heat Energy

doi: 10.3390/en15020435
Suvilahti, a suburb of the city of Vaasa in western Finland, was the first area to use seabed sediment heat as the main source of heating for a high number of houses. Moreover, in the same area, a unique land uplift effect is ongoing. The aim of this paper is to solve the challenges and find opportunities caused by global warming by utilizing seabed sediment energy as a renewable heat source. Measurement data of water and air temperature were analyzed, and correlations were established for the sediment temperature data using Statistical Analysis System (SAS) Enterprise Guide 7.1. software. The analysis and provisional forecast based on the autoregression integrated moving average (ARIMA) model revealed that air and water temperatures show incremental increases through time, and that sediment temperature has positive correlations with water temperature with a 2-month lag. Therefore, sediment heat energy is also expected to increase in the future. Factor analysis validations show that the data have a normal cluster and no particular outliers. This study concludes that sediment heat energy can be considered in prominent renewable production, transforming climate change into a useful solution, at least in summertime.
- Middlesex University United Kingdom
- University of Zurich Switzerland
- University of Vaasa Finland
- Aalto University Finland
- Aalto University Finland
ta212, Technology, Renewable energy, ta114, T, autoregression integrated moving average (ARIMA) modelling forecast, factor analysis, 551, renewable energy, Pearson’s correlations, Autoregression integrated moving average (ARIMA) modelling forecast, fi=Energiatekniikka|en=Energy Technology|, sediment temperature, Factor analysis, Sediment temperature
ta212, Technology, Renewable energy, ta114, T, autoregression integrated moving average (ARIMA) modelling forecast, factor analysis, 551, renewable energy, Pearson’s correlations, Autoregression integrated moving average (ARIMA) modelling forecast, fi=Energiatekniikka|en=Energy Technology|, sediment temperature, Factor analysis, Sediment temperature
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