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PVF Power Generation Forecast System
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Multi-dimensional integration, precise trend identificationIntegrating historical power generation data patterns and incorporating data from multiple sources, such as meteorological and equipment status, not only allows for monitoring long-term power generation trends but also enables flexible adaptation based on real-time variables, laying a solid foundation for power generation planning. |
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Intelligent Mining, Optimizing Power Generation SchedulingLeveraging machine learning algorithms, we automatically mine power generation load cycle patterns from historical data, distinguishing power generation differences during different time periods and operating conditions, identifying peaks and troughs in power generation, and developing scientific scheduling plans in advance to improve energy conversion efficiency. |
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Dynamic correction to address unexpected fluctuationsA real-time data correction mechanism is established to overlay new daily data with historical patterns, allowing for timely fine-tuning of forecast results. This effectively addresses emergencies such as equipment failures and extreme weather, ensuring stable power generation. |
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Decrement Weighting to Activate Data TimelinessApply "time decay" processing to older historical data, giving more weight to recent data in forecasts. This avoids interference from outdated power generation practices, ensures that forecasts are tailored to current power generation scenarios, and enhances the timeliness of decision-making. |
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