Digesting data: Improving the understanding of biogas use through remote sensing
Year: 2025
Type: article
Abstract: The use of small-scale biodigesters is gaining traction in Kenya and Uganda as a clean renewable energy solution. In this paper, we explore how remote monitoring through Internet of Things (IoT) technologies can enhance the service quality of biogas digesters in low-resource settings and promote transparency in carbon accounting. Using data from 121 monitored digesters in Uganda and Kenya, we demonstrate how the analysis of real-time pressure and flow data, coupled with machine learning, can provide an understanding of usage patterns, provide behavioural insights, facilitate the early identification of issues requiring intervention, and give insights into relative digester performance. The tools presented offer a powerful means to study behavioural patterns and the adoption dynamics of biogas as a clean cooking fuel, aligning with national and global aims of broader clean cooking interventions. • Tools were developed to leverage real-time pressure and consumption data from household-scale biogas digesters, providing insights into household cooking behaviours and routines. • A new metric called the biogas utilisation factor was introduced to enable better comparison of how effectively different households use the biogas generated from their digesters. • The K-means anomaly detection algorithm was demonstrated as an effective approach for identifying anomalies in usage behavior using multivariate digester data.
Institutions: Heriot-Watt University, University of Nottingham
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,750
OpenAlex ID: W4409087475