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

Nebiyu Girgibo, Anne Mäkiranta, Xiaoshu Lü and Erkki Hiltunen
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Nebiyu Girgibo: Department of Electrical Engineering and Energy Technology, School of Technology and Innovations, University of Vaasa, 65101 Vaasa, Finland
Anne Mäkiranta: Department of Electrical Engineering and Energy Technology, School of Technology and Innovations, University of Vaasa, 65101 Vaasa, Finland
Xiaoshu Lü: Department of Electrical Engineering and Energy Technology, School of Technology and Innovations, University of Vaasa, 65101 Vaasa, Finland
Erkki Hiltunen: Department of Electrical Engineering and Energy Technology, School of Technology and Innovations, University of Vaasa, 65101 Vaasa, Finland

Energies, 2022, vol. 15, issue 2, 1-28

Abstract: 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.

Keywords: sediment temperature; Pearson’s correlations; autoregression integrated moving average (ARIMA) modelling forecast; factor analysis; renewable energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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