- Digital transformation is having significant impact on the energy sector. This change is driven by new technologies and a demand for faster and better services.
- A new brief describes four big data analytics techniques, some of which are already piloted by the World Bank Group.
- The brief aims to facilitate the use of big data analytics in the energy sector and promote a new online platform to collect and disseminate open energy data.
With unprecedented speed and scale, digital transformation is having significant impact on multiple industries, including the energy sector. The digital agenda in the energy sector is being driven by a combination of technologies, collection and use of data, and a more complex world demanding greater agility, speed and digital competences. It is expected to impact all aspects of the energy sector, including changing patterns of consumption, new ways of optimizing assets, cross-industry partnerships and the greater use of industrial platforms. Digital technologies and ‘smart solutions’ are being placed at the center of new business models and data is seen as an increasingly valuable asset.
The World Development Report 2016 highlighted the potential for digital technologies and data to help bridge the 1.1 billion energy access gap, help speed up the deployment of energy efficiency and increase the reliability and scale of renewable energy integration. The use of Big Data was mentioned as a key enabler. But what is ‘big data’?
Large, heterogeneous, and fast-moving streams of information have emerged as a result of many human activities going digital. To simplify the description of these new types of large data sets they are often described with the short-hand ‘big data’. By nature, the energy sector generates vast amounts of big data through smart meters, sensor networks, customer payments, web server logs, credit history and cell phone call detail records, and satellite imagery.
So, if using big data and digital technologies can help improve energy service delivery - How might the World Bank Group support the uptake of innovative technologies and big data in the energy sector?
A recent solutions brief titled Energy Analytics for Development offers its answers. Financed by the Energy Sector Management Assistance Program (ESMAP), a global knowledge and technical assistance program administered by the World Bank, this publication outlines opportunities to use big data, advanced analytics and open data to solve problems faced by the energy sector today.
Big and Open Data: What is New?
Data analytics have been used in the energy sector for decades. In the last couple of years, new and more powerful tools have been developed as a response to the need to move from traditional analytics to more advanced analytics and deal with these complex and very large data sets called ‘big data’. These new tools – from distributed computing to machine learning and data mining—have evolved to the point where they are now available in commercial settings. With more powerful computing abilities and new analytics techniques, the following four Big Data Analytics Techniques can be used to solve energy sector challenges: description, prediction, detection, and dynamic evaluation.
Classes of Energy Sector Problems Paired with Big Data Analytics Techniques
Recognizing that few energy sector stakeholders are able (or can afford) to collect ALL relevant data, more and more organizations are seeing the value in sharing data. Good practice on how to share data is to use ‘open data principles’ including to ensure that the data is free to access, machine readable and open for reuse. The World Bank Group is supporting the idea of data collaboration and is working to facilitate the sharing of energy data sets. In this regard, the WBG recently released energydata.info , an online platform providing access to datasets and data analytics that are relevant to the energy sector. The platform has been developed as a public good available to anyone interested in sharing data and analytics tools.
How is this Useful in the Real World?
It is clear that big data analytics and open data practices could help advance progress towards supporting energy access, energy efficiency, and renewable energy program development and implementation through the design of new services, policies, and capacity building programs. The solution brief identifies several examples, including:
- Improving reliability and affordability of electricity in Jamaica by addressing non-technical losses from large commercial customer accounts by prototyping a machine learning open software and training utility staff to its daily use.
- Facilitating integration of renewable energies by piloting geospatial least-cost planning together with Earth Institute at Columbia University in selected distribution zones in Nigeria, while ensuring that the sector master plan is digitized and updated in coordination with the distribution plan.
- Supporting energy efficiency programs by developing ‘intelligent’ energy management hardware and software in India that help households and small businesses take control of their energy consumption.
An Opportunity for Emerging Markets, but not Without its Challenges
Despite the many benefits of big data and open data, significant challenges remain to scale up its use in the energy sector globally. These challenges include the cost of data infrastructure upgrades, limited collection and dissemination of data due to poor connectivity lack of data standards creating silos and underutilized digital assets, insufficient capacity to perform analytics and understand the potential value of data, as well as resistance to new technologies.
The integration of digital technologies and use of data in the energy sector might not happen on its own. The solutions brief provides some specific recommendations for how to support the digitization of the energy sector, based on early pilot projects of the WBG and good practices sourced from other groups. Selected recommendations include:
- Governments could contribute by developing regulations around data ownership, best practices for data management and access rights to data. Championing open data principles on national level would also be beneficial.
- Utilities could invest in data infrastructure and analytics capacity and improve their operational efficiency by exploring predictive maintenance, non-technical loss detection, and alternative payment options.
- International organizations could be both funders and conveners to help shape the dialogue. Since local technical capacity is likely to be a key bottleneck, international organizations could help grow in-country capacity. This would include assisting energy stakeholders in building digital infrastructure (software and connectivity infrastructure, such as integrated networks and sensors) and human capacity to collect, store, analyze, and share data through targeted trainings and skills building programs.