nLine Inc.

In much of the world, electricity grids are not instrumented at the customer level,
limiting insights into the power quality experienced by utility customers. Moreover,
to understand grid performance, regulators and investors must depend on utilities
to self-report reliability data. To address these challenges, we introduce PowerWatch,
an agile methodology to directly measure customer experience and aggregated grid performance
without relying on the utility for deployment or management. PowerWatch employs a
system of distributed sensors coupled with cloud-based analytics. We evaluate the
PowerWatch methodology by deploying 462 sensors in homes and businesses in Accra,
Ghana for over a year, yielding the largest open-source data set on electricity reliability
at the customer-level in the region. We describe the architecture, design, and performance
of PowerWatch, as well as the data that are collected, explaining how we determine
the accuracy and coverage of our methodology without ground truth. Finally, we report
on grid performance issues, finding nearly twice as many outages as the utility observed,
suggesting a need for better grid performance monitoring.

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In much of the world, electricity grids are not instrumented at the customer level, limiting insights into the power quality experienced by utility customers. Moreover, to understand grid performance, regulators and investors must depend on utilities to self-report reliability data. To address these challenges, we introduce PowerWatch, an agile methodology to directly measure customer experience and aggregated grid performance without relying on the utility for deployment or management. PowerWatch employs a system of distributed sensors coupled with cloud-based analytics. We evaluate the PowerWatch methodology by deploying 462 sensors in homes and businesses in Accra, Ghana for over a year, yielding the largest open-source data set on electricity reliability at the customer-level in the region. We describe the architecture, design, and performance of PowerWatch, as well as the data that are collected, explaining how we determine the accuracy and coverage of our methodology without ground truth. Finally, we report on grid performance issues, finding nearly twice as many outages as the utility observed, suggesting a need for better grid performance monitoring.

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The vision of sensor systems that collect critical and previously ungathered
information about the world is often only realized when sensors, students,
and subjects move outside the academic laboratory. However, deployments at
even the smallest scales introduce complexities and risks that can be
difficult for a research team to anticipate. Over the past year, our
interdisciplinary team of engineers and economists has been designing,
deploying, and operating a large sensor network in Accra, Ghana that
measures power outages and quality at households and firms. This network
consists of 457 custom sensors, over 3,000 mobile app instances,
thousands of participant surveys, and custom user incentive and
deployment management systems. In part, this deployment supports an
evaluation of the impacts of investments in the grid on reliability and
the subsequent effects of improvements in reliability on
socioeconomic well-being. We report our experiences as we move from
performing small pilot deployments to our current scale, attempting to
identify the pain points at each stage of the deployment. Finally, we
extract high-level observations and lessons learned from our
deployment activities, which we wish we had originally known when
forecasting budgets, human resources, and project timelines. These
insights will be critical as we look toward scaling our deployment to the
en-tire city of Accra and beyond, and we hope that they will encourage and
support other researchers looking to measure highly granular information
about our world’s critical systems.

Content has been formatted as TeX source. Click to format for web.

The vision of sensor systems that collect critical and previously ungathered information about the world is often only realized when sensors, students, and subjects move outside the academic laboratory. However, deployments at even the smallest scales introduce complexities and risks that can be difficult for a research team to anticipate. Over the past year, our interdisciplinary team of engineers and economists has been designing, deploying, and operating a large sensor network in Accra, Ghana that measures power outages and quality at households and firms. This network consists of 457 custom sensors, over 3,000 mobile app instances, thousands of participant surveys, and custom user incentive and deployment management systems. In part, this deployment supports an evaluation of the impacts of investments in the grid on reliability and the subsequent effects of improvements in reliability on socioeconomic well-being. We report our experiences as we move from performing small pilot deployments to our current scale, attempting to identify the pain points at each stage of the deployment. Finally, we extract high-level observations and lessons learned from our deployment activities, which we wish we had originally known when forecasting budgets, human resources, and project timelines. These insights will be critical as we look toward scaling our deployment to the en-tire city of Accra and beyond, and we hope that they will encourage and support other researchers looking to measure highly granular information about our world’s critical systems.

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Incentives are a key facet of human studies research, yet the state-of-the-art
often designs and implements incentive systems in an ad-hoc, on-demand manner.
We introduce the first vocabulary for formally describing incentive
systems and develop a software infrastructure that enables UI-based graphical
generation of complex, auditable, reliable, and reproducible incentive systems.
We call this infrastructure the Open INcentive Kit (OINK).
A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate
incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.

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Incentives are a key facet of human studies research, yet the state-of-the-art often designs and implements incentive systems in an ad-hoc, on-demand manner. We introduce the first vocabulary for formally describing incentive systems and develop a software infrastructure that enables UI-based graphical generation of complex, auditable, reliable, and reproducible incentive systems. We call this infrastructure the Open INcentive Kit (OINK). A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.

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