SolarSense+: AI-Driven Machine Learning Solar Monitoring for Proactive Maintenance

BigWit Energy’s new SolarSense+ platform brings Machine Learning and AI Solar Monitoring to rooftop and ground-mounted solar plants. SolarSense+ continuously analyzes your Sungrow inverter data and weather conditions to establish normal generation patterns. It then detects when output deviates for reasons other than weather – distinguishing routine fluctuations (cloudy skies or soiling) from genuine faults. In this way SolarSense+ can trigger maintenance only when needed, preventing generation losses and downtime. As one industry report notes, AI-powered monitoring can “compare expected energy yield with actual output to highlight underperformance,” pinpointing losses from soiling, shading, or system inefficiencies. SolarSense+ uses exactly this approach, boosting reliability and extending equipment life through preventive maintenance for solar assets.

How SolarSense+ Works

SolarSense+ integrates with Sungrow inverters (internet connection required) to collect real-time performance data. Our cloud-based ML engine learns each plant’s typical output based on location, panel specifications, and current weather. It then continually runs Solar Generation Loss Detection to identify anomalies:

• Pattern Recognition (ML): By applying machine learning models to inverter SCADA data (voltage, current, temperature, etc.), SolarSense+ learns normal generation curves. It automatically accounts for predictable factors like time of day and weather. When actual yield diverges from the expected curve, SolarSense+ evaluates the cause.

• Cloudy vs. Dirty vs. Fault: The system uses weather forecasts and soiling models to tell if a drop in generation is due to clouds or dirt, rather than equipment trouble. For instance, on an overcast day low output is normal, but if it’s sunny yet yield is down, SolarSense+ checks for soiling patterns or fault signatures. This prevents “false alarms” – we only flag real issues.

• Automated Alerts: Once an issue is identified, SolarSense+ automatically generates a maintenance alert. For example, if panel dirt is the culprit, it will notify the owner to clean the array; if it detects an electrical fault (like a grid fault, hot-spot or inverter anomaly), it will dispatch a service call. This proactive, preventive maintenance approach helps plant owners act before minor issues become major breakdowns.

• Built on Sungrow Smart O&M: Sungrow’s multi-string inverters already include advanced diagnostics – they can detect component issues and send warnings for proactive maintenance. SolarSense+ takes this further by applying cloud AI. For example, Sungrow’s “smart IV curve diagnosis” flags anomalies; SolarSense+ complements it by analyzing system-wide trends over weeks or months, tying together data across multiple inverters and strings.

• Connected System: Because SolarSense+ is cloud-based, it needs an internet link. Your Sungrow inverter streams data via Wi-Fi or Ethernet to SolarSense+’s servers. From there, the AI does the heavy lifting. This is why SolarSense+ currently works only with Sungrow inverters (to ensure reliable data connectivity and integration).

Note: Sungrow inverters already support remote monitoring (e.g. via iSolarCloud). SolarSense+ leverages that data feed to apply machine learning and predictive logic, turning routine telemetry into actionable insights.

Reducing Generation Loss and Improving Plant Health

By catching issues early, SolarSense+ drives higher energy yields and healthier plants. Unchecked small problems can add up: for example, soiling (dust/grime on panels) can slash output by 20–30% in dusty environments. One case study of a 10 MW solar plant found soiling was costing over 20% of potential production (≈1/5 of energy lost). Cleaning on a fixed schedule proved ineffective, but an AI-driven approach restored significant yield. In fact, owners using smart monitoring saw generation boosts of 5–15% after optimizing cleaning and maintenance schedules. SolarSense+ embeds that insight: it computes when cleaning or servicing actually pays off (by modeling lost revenue vs. maintenance cost), and only then alerts the crew.

Figure: Example SolarSense+ dashboard showing actual vs. expected generation (the purple gap indicates soiling loss). AI analytics flag the point at which cleaning restores full power.

Key benefits include:

• Maximized Output: Continuous Solar Generation Loss Detection means you won’t unknowingly lose power. As AI compares expected vs. actual yield, it spots underperformance immediately. This means you capture every possible kWh. (Studies show just 5–15% more energy can result from data-driven maintenance)

• Lower Downtime: By “predicting” faults before they happen, SolarSense+ dramatically reduces unplanned outages. As one AI-in-solar analysis notes, predictive maintenance “reduces downtime and operational disruptions” by scheduling fixes during off-peak times. Early fault alerts keep panels online longer and preserve revenue.

• Preventive Maintenance for Solar: Instead of reactive repairs, technicians perform maintenance on a schedule informed by data. Routine cleaning or part replacement only happens when analytics show it’s needed. This optimizes costs — for instance, one project cut cleaning expenses 40% while boosting generation.

• Longer Equipment Life: Addressing issues promptly (e.g. fixing a loose wire before it burns out) extends inverter and panel lifetimes. Constant monitoring means no component remains in a degraded state for long.

• Visibility & Insights: SolarSense+ provides clear reports and dashboards so owners see exactly why an alert fired. For example, it can break down losses into categories (soiling, shading, mismatch, downtime, etc.), helping teams focus on the real problems. This transparency is at the heart of smarter solar operations.

BigWit Offer: All BigWit Energy customers get SolarSense+ free for the first year, then at 50% off from year two onward. This special pricing makes it cost-free to try SolarSense+ and see the savings firsthand. (SolarSense+ simply uses your existing Sungrow inverter and internet – no extra hardware needed.)

Customer Success Stories

Residential Example – Mr. Deepak’s Home Rooftop (15 kW, Sungrow): Deepak’s household system was underperforming by ~10% but he didn’t know why. After enabling SolarSense+, the system flagged that on clear days his output was lower than expected. It turned out a morning shadow from a nearby tree was trimming production. SolarSense+ alerted Deepak to trim the foliage, which immediately increased output. A month later SolarSense+ detected another anomaly: one panel string’s voltage curve looked “flatter” than normal (indicating micro-shading). A quick panel inspection found a broken connector which was replaced under warranty, restoring the string’s output. In both cases, preventive action kept Mr. Deepak’s system healthy. Overall his yield improved ~8% in the first year and he avoided a full system checkup.

Commercial Example – MD Housing Ltd (50 kW, Sungrow SG30MX): MD Housing rooftop array had no monitoring beyond basic meters. After installation, SolarSense+ immediately flagged reduced generation after prolonged dry weather. It calculated that the soiling loss was equivalent to ~Rs3259/month in lost revenue. An automatic maintenance ticket was generated to clean the panels (instead of waiting for the usual seasonal clean). Cleaning was done just before peak season, and generation jumped by 12% post-clean. Later, SolarSense+ noticed a slight repeatable dip every afternoon, unlike normal behavior. It determined this matched an inverter overheating signature. Technicians opened a ventilated panel, found a clogged fan, and fixed it before a full midday outage occurred. Thanks to SolarSense+, MD Housing keeps its mall lights bright even on sweltering days.

Industrial Example – Spark Manufacturing (100 KW plant, Sungrow SG125CX): This large factory installation powers critical processes. SolarSense+ was set up during commissioning. Within weeks, the system predicted a coming inverter failure by noticing a gradual drop in inverter efficiency (DC–AC conversion) beyond normal wear. It alerted the operations team, who scheduled a parts replacement during the night shift. Without intervention, that inverter would have tripped under peak load, causing a more costly shutdown. Over a year, Spark Electricals saw zero unscheduled outages on their solar side. Their plant managers report that SolarSense+ effectively “acts as a full-time virtual technician,” spotting issues that human checks might miss. According to industry experience, avoiding even one major inverter failure can save hundreds of thousands in lost production and repair costs.

SolarSense+ and BigWit’s Mission

SolarSense+ fits squarely within BigWit Energy’s mission of making solar installations smarter, more reliable, and sustainable. As BigWit states, it “leverages advanced technology and data-driven insights to ensure reliable, efficient, and sustainable energy solutions for a brighter tomorrow” bigwitenergy.com. In other words, BigWit empowers customers with cutting-edge tools like SolarSense+ so every solar plant operates at its best. By adding AI Solar Monitoring and machine learning diagnostics to the ecosystem, BigWit continues its tradition of innovation – giving both homeowners and businesses peace of mind that their panels are always watched over intelligently. As one expert wrote, integrating AI into solar is a “game-changer” that lets systems predict failures and dynamically adjust to conditions, ensuring every ray of sunlight is harnessed efficiently. SolarSense+ is BigWit’s concrete step toward that smarter future.

In summary, SolarSense+ brings Machine Learning into solar O&M to automatically catch small issues before they become big ones. It turns raw data from Sungrow inverters into actionable maintenance plans – cutting losses, boosting output, and making solar systems truly smarter. With SolarSense+ running in the background (free for year one!), BigWit Energy customers can sit back and watch their solar plants perform better than ever.