New study on the impacts of lockdown on residential electricity demand in India, demonstrates the need for robust public data quality as part of the nation's digitalisation policy, writes Ramit Debnath.
India is the world’s third-largest energy-consuming country and emitter of carbon dioxide, despite low per capita carbon dioxide emissions. India’s continued industrialisation and urbanisation will make enormous demands for its energy sector and policymakers to enable low-carbon and just transition. Moreover, climate change is bound to increase the frequency of heatwaves in India, causing further strain in the energy system; for instance, electricity demand for cooling appliances like air-conditioners is expected to increase six-to-eight folds by 2040.
Greater energy efficiency will be part of the solution, and digitalisation has a big role to play, making it a critical tool for climate action. The shift in working patterns during the pandemic has provided a natural experiment that holds lessons for how smart meters can encourage greater energy efficiency.
In a recent paper we used the Government of India’s public smart meter database, called NEEM, to evaluate how lockdown impacted residential electricity demand in 13 cities. Due to changing working norms, it demonstrated distinct shifts in electricity usage patterns across different apartment types.
The study explored the effect of covid-19 measures on daily peak electricity demand using advanced artificial intelligence (AI)-driven algorithms, finding that work-from-home significantly shifted users’ energy use behaviour compared to 2018 and 2019 levels. Thus digitalisation efforts like the NEEM platform have been instrumental in studying the consequences of the lockdowns for energy use. It will also be critical for evaluating future policies associated with climate action like energy efficiency and energy affordability.
For instance, the study found that during the lockdown in 2020, maximum peak energy demand exceeded by almost 150–200% across one-room units, one-bedroom units and two-bedroom apartment units. These types of housing are typical for low- and middle-income households. Such a demand increase is large enough to unexpectedly raise households’ monthly electricity bills by five or 10 times, which was observed in many megacities during the first lockdown months. Thus the availability of such digital data in the public domain can help policymakers plan for post-pandemic energy demand management in possible future hybrid working scenarios.
We cannot yet be sure whether hybrid working conditions will be the new normal. Still, for now, what we have learned from the study is that the digitisation of the energy system through smart meters can enable the understanding and management of energy demand.
The digitalisation efforts in India are still at an experimental stage, which means there are issues about data quality and reliability. This is not so much about the energy usage as about a lack of information on the demographic and climate-related factors. Linking data in this way would enable exploration of the correlations between room size, income categories, and energy consumption in work-from-home circumstances. Making this kind of information available could empower citizens, policymakers and researchers to be more aware about their own energy use and the system as a whole. It would provide opportunities for the government to create better digital energy infrastructure for public use.
Incomplete datasets and lack of contextualised information on local weather, income, age, electricity bills and household appliance ownership pose serious challenges for the research, which had to make several simplifying assumptions, limiting the generalisability of this research.
Nevertheless, the study offers lessons not only for India but also for other developing nations planning for the digitalisation of their energy system. Digital tools in government hold out the promise of better evidence-based policymaking if they can turn the data into actionable information. The NEEM database meant that the abrupt shifts in residential electricity demand due to the pandemic could be monitored and linked to housing types. Being able to monitor such patterns in real time will enable greater energy efficiency when demand is increasing, and – if linking other kinds of data becomes possible – to develop other policy interventions to help low-income households.
The study is an outcome of an interdisciplinary collaboration between the University of Cambridge, UK (Dr Ramit Debnath, Dr Ronita Bardhan and Prof Michael Ramage), Lawrence Berkeley National Lab, USA (Dr Tianzhen Hong), Carnegie Mellon University, USA (Dr Ashwin Misra) and International Energy Agency, France (Vida Rozite).
The views and opinions expressed in this post are those of the author(s) and not necessarily those of the Bennett Institute for Public Policy.
