Using solar panels for business purposes: Evidence based on high-frequency power usage data. (2021)

• Record Type: Journal Article
• Authors: Weisser, Christoph; Lenel, Friederike; Lu, Yao; Kis-Katos, Krisztina; Kneib, Thomas
• Is Part Of: Development engineering. Volume 6(2021)
• Publication Date: 2021
• Subjects: Rural electrification -- Off-grid energy -- High-frequency electricity usage data -- Solar panels -- Tanzania -- Risk management -- Credit default -- Big data -- Supervised machine learning -- Time-dependent proportional hazards model -- XGBoost; Technical assistance -- Periodicals; Economic development -- Periodicals; Engineering -- Social aspects -- Periodicals; 338.9105
• DOI: 10.1016/j.deveng.2021.100074
• Languages: English
• ISSNs: 2352-7285

Abstract

Abstract: Access to electricity is typically the main benefit associated with solar panels, but in economically less developed countries, where access to electricity is still very limited, solar panel systems can also serve as means to generate additional income and to diversify income sources. We analyze high-frequency electricity usage and repayment data of around 70, 000 households in Tanzania that purchased a solar panel system on credit, in order to (1) determine the extent to which solar panel systems are used for income generation, and (2) explore the link between the usage of the solar system for business purposes and the repayment of the customer credit that finances its purchase. Based on individual patterns of energy consumption within each day, we use XGBoost as a supervised machine learning model combined with labels from a customer survey on business usage to generate out-of-sample predictions of the daily likelihood that customers operate a business. We find a low average predicted business probability; yet there is considerable variation across households and over time. While the majority of households are predicted to use their system primarily for private consumption, our findings suggest that a substantial proportion uses it for income generation purposes occasionally. Our subsequent statistical analysis regresses the occurrence of individual credit delinquency within each month on the monthly average predicted probability of business-like electricity usage, relying on a time-dependent proportional hazards model. Our results show that customers with more business-like electricity usage patterns are significantly less likely to face repayment difficulties, suggesting that using the system to generate additional income can help to alleviate cash constraints and prevent default.