SmartHelio helps solar utilities automatically predict and prevent downtime disruptions, increasing their annual revenue by 10% from their solar plants. We are working with 15 large solar utilities to help them generate extra $25M per MW per year.
Amazing and easy to use tool to help maintaining and predicting your solar energy output in a very cost-effective manner! Great and easy to understand analytics - visualized in real-time. It’s an award-winning Swiss Technology that can be used for any size of PV assets.
Hi all, I am Govinda CEO of SmartHelio.
We are really excited to launch 4 powerful analytics tools that can help solar utilities automatically predict and prevent solar plant failures and increase their overall performance.
We have taken the 4 most pressing challenges faced by the solar PV industry today (bad and missing data, inaccurate weather forecast, uneven & unknown panel degradation, and ineffective cleaning schedule) and designed easy-to-use tools with one click to solve these challenges. In the making of these tools, we have used our subject expertise - understanding of PV System Engineering, weather modeling, and climate change modeling, and coupled it with advanced computational methods and machine learning.
Please give your comments. Happy to answer your queries.
Tool [3/4]: Dynamic Cleaning Scheduler
Purpose of the Tool: The wide-area spread of the solar plants creates operational challenges for the solar asset managers. In most cases, the soil disposition across the plant is non-uniform and dynamic in nature. Moreover, meteorological events (like rainfall, snow, dust storm, etc.) play a vital role in the overall deposition of soil; hence, these factors should also be considered in the cleaning schedule. A fixed cleaning schedule that normally does not cover these factors, leads to inefficient cleaning and eventually leads to significant loss of energy production and resources (time, water, and human).
Creators: @kritz03 & @dorianguzman
The Methodology: The Dynamic Cleaning Schedule helps solar asset managers explore the most optimal timing for cleaning. This helps to maximize the energy production, reduce the cleaning cost and minimize the usage of water used for cleaning the solar panels.
Dynamic Cleaning Scheduler considers the historical & current soiling trends, meteorological & cleaning events, weather forecast, and the cost of cleaning and follows an AI-based optimization process to generate a dynamic cleaning schedule for different sections of the plant.
Tool [1/4]: AI-based Data Sanitization Service
Purpose of the Tool: The data acquisition system deployed in the solar industry frequently suffers from sudden communication failure and monitoring equipment malfunction. This leads to significant missing data sets and a lot of bad values in the system database. Therefore, any analysis based on these data sets leads to inaccurate results, higher assumptions, and bad conclusions.
Creators: @bhavya_dureja & @dorianguzman
The Methodology: The algorithm is formulated in a way that it first removes all the bad/corrupt data which are called as outliers using the PV system’s configuration and expected performance behaviour. Next, it identifies the data gaps which could be due to network/communication errors, bad weather data, or fault measurement devices. Finally, using AI & Machine Learning algorithms, the model first learns trends and seasonality of the data and later predicts these data gaps with high accuracy to reduce data loss.
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