Decision Support System for Regional Domestic Energy Planning

T. V. Ramachandra1,* , S. Vamsee Krishna2 and B. V. Shruthi2
1 Energy Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560 012
2 Centre for Sustainable Technology, Indian Institute of Science, Bangalore 560 012


Among the 11 taluks, 2500 randomly selected households from 137 villages of Kolar district were studied. Survey shows that most of them still use tra­ditional fuel wood stoves for cooking (97.92 %) and water heating (98.3 %). Average fuel wood consump­tion for cooking ranges from 0.69±0.34 to 1.42±0.27 kg/capita/d, while, for water heating, it ranges from 0.11±0.07 to 0.71±0.25 kg/capita/d. Analysis of other sources of energy for domestic purposes shows that electricity, kerosene, LPG, and biogas are used for cooking, water heating and lighting in a rational manner.

Most energy planning exercises are carried out with aggregate data at the national level. At regional level namely village, block or district, there have been fewer efforts for energy planning. Energy resources and demand are spatially distributed. Aggregated analysis does not capture the spatial variation in supply and demand. DSS assists in analyzing the energy sources and demand spatially. The technologies and methods used to develop and deploy DSS to aid in domestic energy consumption make work easier for a decision maker. The possibility of quickly accessing and processing large spatial databases over high speeds, offers a tremendous improvement. In spite of rapidly advancing computer technology and the proliferation of software for decision support, relatively a few DSS have been developed for assessment of energy demand. PSS with a user-friendly GUI provides user an easy access of data analysis and the design and evaluation of domestic energy consumption strategies. The entire framework is designed in such a way that, user is provided with helpful tips and context-sensitive help options. Energy DSS will improve the quality of decision making at the block, district, and State level and enable the analysis and understanding of energy impacts of various decisions.


Authors thank Ministry of Science and Technology, Govt of India, New Delhi and Indian Institute of Science, Bangalore, for the infrastructure support and financial assistance. Authors also thank Shri H Lakshminarayana, Shri K Venu Gopal and Shri Pramod S Dabrase for field data collection.

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