Solar Panel Soiling Forecasting for Profitable Cleaning Timing

Solution Overview

Problem

Conventional methods for determining the optimal cleaning schedule for solar panels are inefficient due to the variability of factors such as rainfall and seasonal pollen accumulation, leading to suboptimal cleaning decisions that do not consider future soiling or weather patterns.

Innovation Solution

A software tool using Monte Carlo simulations based on historical precipitation data, PV production data, and soiling-rate data to forecast photovoltaic output loss and recommend the earliest profitable cleaning date, where the cost of cleaning is less than or equal to the recoverable soiling loss over a 90-day period.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If solar panels are cleaned frequently to maintain high output, then photovoltaic output is improved, but cleaning costs increase

Engineering Contradiction:

Improvephotovoltaic outputVSAvoidcleaning costs

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary forecasting of soiling losses using Monte Carlo simulations before cleaning decisions are made. By predicting future soiling rates, rainfall patterns, and their impact on photovoltaic output, the system determines the optimal cleaning date in advance, ensuring cleaning is performed only when the cost of cleaning equals or is less than the recoverable soiling loss, thus avoiding unnecessary cleaning expenses while maintaining output

Inventive Principle:

Principle #10Preliminary action

2Loss of time

If cleaning decisions are made without considering future soiling and weather patterns, then decision-making speed is improved, but cleaning effectiveness deteriorates

Engineering Contradiction:

Improvedecision-making timeVSAvoidcleaning effectiveness

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system performs preliminary forecasting of soiling losses using Monte Carlo simulations before cleaning decisions are made. By predicting future soiling rates, rainfall patterns, and their impact on photovoltaic output, the system determines the optimal cleaning date in advance, ensuring cleaning is performed only when the cost of cleaning equals or is less than the recoverable soiling loss, thus avoiding unnecessary cleaning expenses while maintaining output

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors actual photovoltaic output, soiling rates, and weather conditions, then feeds this data back into the Monte Carlo simulation model to refine future predictions. This feedback loop ensures that cleaning decisions are based on accurate, up-to-date information, improving both the speed and reliability of decision-making by dynamically adjusting forecasts based on observed patterns

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240380361A1Soiling mitigation for solar panels

Publication Date: 2024.11.14 DUKE ENERGY CORP

AI summary

Methods for solar-panel-soiling mitigation are provided. A method for solar-panel-soiling mitigation includes displaying, via a graphical user interface (GUI) on an electronic device, a predicted amount of photovoltaic (PV) output that will be lost due to soiling of solar panels that share a site. Moreover, the method includes displaying, via the GUI, a recommended date for cleaning the solar panels. The recommended date is an earliest calendar date on which a cost of cleaning the solar panels is less than or equal to a cost of the predicted amount of PV output that will be lost due to soiling.