en.Wedoany.com Reported - About 15 years ago, Abhay Gupta founded Bidgely in the United States with the goal of unlocking the untapped potential of smart meter data. He developed a technology called load disaggregation, also known as Non-Intrusive Load Monitoring (NILM), which works by analyzing meter data to identify the unique energy consumption signatures of each appliance, thereby determining which appliances are being used, when, and how much energy they consume.
Abhay Gupta, founder and CEO of Bidgely, shared further insights. He noted that utility companies previously had limited knowledge of their customers, only possessing meter reading data, whereas smart meters provide granular interval data (hourly, every 30 minutes, or even shorter) containing valuable information. Bidgely, as a machine learning company based on load disaggregation technology, uses algorithms to break down a household's total electricity consumption into major appliances such as air conditioners, water heaters, and refrigerators, generating an energy profile for each home. This technology transforms electricity consumption from a "black box" into something visible, similar to the itemized details on a credit card bill. Initially, the solution focused on customer engagement and energy efficiency programs, but over the past five years, it has expanded into areas such as grid planning, peak load management, electrification projects, demand response, virtual power plants, call centers, and high bill analysis. Bidgely serves electric, gas, and water utility companies worldwide through a cloud-based SaaS platform. These companies provide meter data, and the algorithm outputs insights to support decision-making.
In India, Bidgely manages approximately 50 million smart meters, making it a key growth market. It has partnered with three distribution companies and one private utility company. Its offerings cover theft detection and consumer-facing customer engagement. Theft detection is currently the primary deployment use case. In addition to their monthly electricity bills, consumers receive disaggregated energy consumption insights for appliances like refrigerators, air conditioners, and water heaters, and can compare their usage with similar neighboring households. This leverages the psychology of social comparison to encourage energy savings. The platform provides actionable insights into usage patterns, such as the daily operating hours of air conditioners, helping consumers identify inefficient behaviors and reduce their electricity bills. India's Ministry of Power aims to demonstrate the tangible benefits of smart meter investments to consumers. Given that energy expenditure accounts for a certain proportion of household income, efficiency improvements can yield significant financial returns.
India suffers an estimated billions of dollars in non-technical losses annually due to electricity theft. Bidgely leverages its load disaggregation expertise to develop solutions that help distribution companies identify high-probability cases of theft. The platform provides a prioritized list of consumers most likely to be involved in theft, supporting more targeted field investigations. Methods of theft include tampering with meters, bypassing meters, unauthorized connections, and misuse of tariff categories. The algorithm achieves high-accuracy fraud detection by analyzing usage patterns and anomalies in meter data.
Regarding grid resilience, Abhay Gupta explained that the proliferation of rooftop solar introduces volatility, while electric vehicles significantly increase residential electricity demand. A typical household has a normal load of 2-3 kilowatts, but charging one electric vehicle can add approximately 8 kilowatts, bringing the total demand to 10-11 kilowatts; charging two vehicles simultaneously could approach 18 kilowatts. Distribution transformers are designed for 30-50 households. If multiple households charge their vehicles simultaneously at night, the load could exceed the limit. Over the past few years, transformer costs have doubled, and lead times have extended to nearly three years, prompting utilities to rely on data-driven planning. Bidgely's software can automatically detect electric vehicle charging signatures from meter data, aggregate information across households, transformers, and feeders, and forecast load growth over the next 12 to 36 months. This information supports utilities in deploying load shifting programs or demand response schemes, encouraging off-peak charging through time-of-use or dynamic pricing, or allowing customers to authorize control over charging times to stagger peak demand. This approach can disperse peak demand, delaying capital-intensive transformer upgrades by four to seven years and enabling planned grid modernization.
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