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With rolling blackouts, the outdated grid, and increasing rates, people are left powerless. Solar changes that. It decentralizes power, putting the power back into the hands of the people. It’s less taxing on the grid and paves the way for a more sustainable future.
Despite attempts by power companies around the nation to stop net metering and hinder solar growth, solar is still expanding. The quarterly SEIA/Wood Mackenzie Power & Renewables U.S. Solar Market InsightTM showed that in Q3 2021, the U.S. solar market increased by 33 percent (the largest Q3 on record). Solar isn’t going anywhere. Data Aggregation at Light Speed The landscape for storing and processing data, especially in the solar power and home device industry, has shifted dramatically in the last couple of decades. With the expansion of internet connectivity and cloud storage, new smart home devices are constantly sharing consumer information to the cloud. The advent of technology (e.g., Kafka) for handling high-volume data streams—with near real-time latencies—has enabled the rapid aggregation of collected data that can automate business processes and predictions. The potential of rapid data collection and aggregation is accentuated when events happening in the field, from power-generation changes, home-automation device commands, and more, can be responded to within seconds. Managing Energy In-Home With Technology Access to constant, reliable solar-generated power helps consumers with smart home devices proactively manage the energy demands in their home. By using the data shared to the cloud by the devices at near real-time, combined with weather forecasts and cyclical demand models, a personalized prediction of the power generation capacity over a specific time period is created. To accurately predict and manage energy usage, the importance of statistical modeling and inference over ever-larger datasets continues to increase. Leveraging data to drive business strategies becomes more economical as cloud providers improve the abstraction level at which their machine-learning tooling operates. Just five years ago, it would have been nearly impossible to build complex, enhanced forecasts without a large team. Additionally, the IoT sector has gained a fair amount of domain-specific tooling over the last 10 years. The infrastructure of most of the major cloud providers is now tying high-volume network endpoints to standard stream processing and archival tools. This is making it feasible for businesses to start collecting information from huge numbers of in-the-field devices with relatively meager up-front planning and costs. As a result of these technological advancements, businesses are able to focus on innovation to expand their products with smaller, more nimble teams.Access to constant, reliable solar-generated power helps consumers with smart home devices proactively manage the energy demands in their home
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