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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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Replacing traditional stoves with advanced alternatives that burn more cleanly has the potential to ameliorate major health problems associated with indoor air pollution in developing countries. With a few exceptions, large government and charitable programs to distribute advanced stoves have not had the desired impact. Commercially-based distributions that seek cost recovery and even profits might plausibly do better, both because they encourage distributors to supply and promote products that people want and because they are based around properly-incentivized supply chains that could more be scalable, sustainable, and replicable. The sale in India of over 400,000 “Oorja” stoves to households from 2006 onwards represents the largest commercially-based distribution of a gasification-type advanced biomass stove. BP's Emerging Consumer Markets (ECM) division and then successor company First Energy sold this stove and the pelletized biomass fuel on which it operates. We assess the success of this effort and the role its commercial aspect played in outcomes using a survey of 998 households in areas of Maharashtra and Karnataka where the stove was sold as well as detailed interviews with BP and First Energy staff. Statistical models based on this data indicate that Oorja purchase rates were significantly influenced by the intensity of Oorja marketing in a region as well as by pre-existing stove mix among households. The highest rate of adoption came from LPG-using households for which Oorja's pelletized biomass fuel reduced costs. Smoke- and health-related messages from Oorja marketing did not significantly influence the purchase decision, although they did appear to affect household perceptions about smoke. By the time of our survey, only 9% of households that purchased Oorja were still using the stove, the result in large part of difficulties First Energy encountered in developing a viable supply chain around low-cost procurement of “agricultural waste” to make pellets. The business orientation of First Energy allowed the company to pivot rapidly to commercial customers when the household market encountered difficulties. The business background of managers also facilitated the initial marketing and distribution efforts that allowed the stove distribution to reach scale.

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Abstract-We present a new inter-domain traffic engineering protocol based on the concepts of Nash bargaining and dual decomposition. Under this scheme, ISPs use an iterative procedure to jointly optimize a social cost function, referred to as the Nash product. We show that the global optimization problem can be separated into sub-problems by introducing appropriate shadow prices on the inter-domain flows. These sub-problems can then be solved independently and in a decentralized manner by the individual ISPs. Our approach does not require the ISPs to share any sensitive internal information (such as network topology or link weights). More importantly, our approach is provably Pareto-efficient and fair. Therefore, we believe that our approach is highly amenable to adoption by ISPs when compared to past naive approaches.We conduct simulation studies of our approach over several real ISP topologies. Our evaluation shows that the approach converges quickly, offers equitable performance improvements to ISPs, is significantly better than unilateral approaches (e.g. hot potato routing) and offers the same performance as a centralized solution with full knowledge.

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