Rooftop solar systems are most effective when they’re designed to meet the requirements of the household or commercial consumer. This is done by aligning electricity production of the solar system with the usage patterns of the consumer. Greater overlap results in better financial outcomes for the consumer.
Most solar designers do not conduct a thorough analysis of consumer’s usage patterns (their load profile), it is a complex and time consuming task. The best results come from analyzing raw half hourly data that is logged from the consumer’s electricity meter. The data comes in a large .xlsx or .csv format. Solar designers must then appropriately sort this data and graph the information in Excel to understand usage patterns. This requires the solar designer to have extensive knowledge of Excel and spend multiple hours or even days of shifting through raw data.
Alternatively there are existing tools that predict patterns based on the consumer’s demographic. This reduces the complexity of the analysis but mean the results are significantly less accurate.
Based on market research, solar designers will ignore analyzing the usage patterns due to the complexity with raw data and lack of accuracy with models and choose to input a fixed self consumption based on their personal experience.
The feature to Simulate Load Profiles from User Uploaded Interval data is designed to lower complexity and barrier of entry to using consumer electricity data. The solar designer can simply upload the raw electricity data and Pylon Observer will sort, filter and display the information in clear and easy to interpret graphs. Recent upgrades has allow the electricity data to be simulated against the solar production of the designed solar system. We’re now able to know exactly how installing solar will offset consumer’s electricity usage and very accurately predict future outcomes.
This is a significant upgrade for the industry; analysis of raw data which took would hours (potentially days) using excel is now instant. The ease of use has resulted in more solar designers using consumer electricity data in their design process and thus improving outcomes for end consumers. We’ve also seen an increase in our customer’s sales conversions as it is now easier to provide strong financial evidence of the benefits of solar.
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