My second academic paper, which has been submitted for peer review, looks at the use of open-source software, open and crowd-sourced data to support the energy planning process, in particular in the developing world. OK, it’s not everyone’s idea of a page-turner, but eight other authors and I thought it was an important topic!

Decisions on energy policies and associated investment are among the most difficult facing countries – particularly in developing economies. On them may depend billions of dollars, and even determine whether energy services are available to the poor.

The paper, Open Source Software And Crowdsourcing For Energy Analysis, co-authored by nine experts on energy decision-making, examines how open data and so-called “crowdsourcing” – the out-sourcing of tasks to a distributed group of people – can assist developing countries. Governmental acceptance and adoption of open data has been growing rapidly with examples ranging from the US and the UK, to Kenya and Ghana.

The document provides a survey of existing research, and also explores the potential role that linked, open data can play in both supporting analysis, and in enhancing public engagement with energy issues. The paper argues that open modelling efforts can improve the utility and accessibility of energy models, and also lower the cost of data collection and management.

“Applying these innovative tools and methods into energy sector analytics will considerably help the job of policy-makers and investors. It will also require ongoing international support” said Morgan Bazilian, the paper’s lead author.

In April 2012, Robert Zoellick, President of the World Bank, ‘tweeted’, “Open information, open data, and open access to knowledge may turn out to be the most important legacy of the past five years.” Still, the transformative impacts of applying open source software (OSS) and data as well as associated training tools are in the early stages of adoption in the area of energy system analysis.

The paper’s authors are Morgan Bazilian^a,b, Andrew Rice^c, Juliana Rotich^d, Mark Howells^e, Joseph DeCarolis ^f, Stuart Macmillan^g,h, Cameron Brooksⁱ, Florian Bauer^j, and Michael Liebreich^k. The paper is being submitted for publication in peer-reviewed literature, and comments are welcome on the draft.

Please download the paper here: http://www.bnef.com/WhitePapers/download/108

(a) United Nations Industrial Development Organisation, Vienna, Austria

(b) International Institute for Applied Systems Analysis, Laxenburg, Austria

(c) Computer Laboratory, University of Cambridge, Cambridge, UK

(d) Ushahidi, Nairobi, Kenya

(e) Royal Swedish Institute of Technology, Stockholm, Sweden

(f) North Carolina State University, North Carolina, USA

(g) Stanford University, California, USA

(h) National Renewable Energy Laboratory, Colorado, USA

(i) Tendril Networks, Colorado, USA

(j) Renewable Energy and Energy Efficiency Partnership, Vienna, Austria

(k) Bloomberg New Energy Finance, London, UK