SmartHelio technology partner in Project GENTE

SmartHelio technology partner in multi-national project - GENTE

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multi-national projects like GENTE by ERA-Net Smart Energy Systems'

We are proud to announce that SmartHelio is a Technology partner of an international research project GENTE. GENTE brings together an international consortium of highly qualified partners comprising need owners, citizens, scientific partners, technology providers, living labs, and technology demonstration sites. 

GENTE is a multi-million dollar, multi-national project. The project aims to develop services and applications (the LEC toolbox) for Local Energy Communities (LECs) to improve their abilities to optimize, control, and manage energy resources in a federated manner. It leverages advanced technologies, including the internet of things (IoT), distributed ledger technology/Blockchain, edge processing, and artificial intelligence for autonomous energy resource management. The LEC toolbox will automatically implement the optimization strategies to control and manage the energy mix at the LEC level. It will minimize the grid dependencies for local power needs, significantly reduce the power curtailment by the grid, and maximize the injection of electricity into the grid considering the flexibility and stability provisions of the network.

The project also aims to bring intelligence to distributed energy assets by considering users’ behaviors, data privacy, and interoperability. The project will deliver a decision support tool and innovative services to the LECs that will enhance the economic viability of LECs and promote engagements of end-users and self-governance. The project will be implemented and validated in 6 different sites across three different countries. 

The GENTE project has received funding in the framework of the joint programming initiative ERA-Net Smart Energy Systems’ focus initiative Digital Transformation for the Energy Transition, with support from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 883973.

 

Disclaimer: The content and views expressed in this article are those of the authors and do not necessarily reflect the views or opinion of the ERA-Net SES initiative. Any reference given does not necessarily imply the endorsement by ERA-Net SES.

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HelioCloud - Your solar plant on autopilot

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Solar Photovoltaic is the most preferred alternative clean energy asset. In the last couple of years, it has seen a massive deployment across the world with an average annual capacity addition of 180 GW which is expected to triple by 2030. The growing capacity of solar photovoltaics is great news but contrarily, it is also making the asset management a tedious job and, in the existing ecosystem, it is bringing down the operational efficiency of asset maintenance which is subsequently limiting the overall performance level of the PV plants. This is a worrying phenomenon for investors. The existing asset management tools and Standard Operating Procedures (SPOs) are limited and not effective enough to utilize the possible potential of the solar energy assets. Some of the major problems can be summarized below:

  1. Unable to detect the early signals of equipment failure or faults in the system which leads to system underperformance because of unknown reasons till it becomes significant enough to attract the plant/asset manager’s attention. But by this time the asset has already lost a substantial revenue
  2. High manual dependence on the O&M process limits the O&M teams from acting on time which leads to a higher turn-around time from the Fault Appearing to Fault Resolved. In industry, in some cases, we have seen a fault resolution period of up to several months.
  3. The existing PV Asset Management practices are not optimized enough to maximize energy production and revenue. In most cases, the maintenance schedules depend on the convenience of the ground staff which leads to inefficient asset maintenance, system underperformance, and high O&M expenses.

world's first real-time analytics software - HelioCloud

We strongly believe inefficiencies related to asset management and O&M can be handled by introducing effective digitization, AI, and automation practices. In our efforts, we have introduced An Autopilot for the solar PV industry. The Autopilot works as a virtual Asset Manager, Performance Manager, and as Plant Manager. It continuously reads the plant performance data, weather data, satellite data, and any other 3rd party data that is beneficial for doing the performance assessment and root cause identification in real-time. Autopilot has a robust understanding of the PV system engineering and it uses machine learning to develop incremental intelligence over a period of time which makes it incredibly accurate and fast in terms of detecting the early signatures of system underperformance, predicting potential faults, quantifying the impact of existing/upcoming faults/issues and dynamically scheduling the on-ground interventions by technical work-force (human and machines). With Autopilot we can see an overall improvement in solar assets’ performance by 15% (over their lifetime) and a reduction in O&M and Asset management costs by more than 50%. 

As a stepping stone into the future of solar photovoltaics and other alternative clean energy assets, we believe if machines can manage themselves and can autonomously operate thousands of miles away in the extraterrestrial space then why can’t solar plants run autonomously with software like Autopilot. We see a vision to put each and every solar energy and other clean energy asset in Autopilot mode once they are commissioned.

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Predict solar irradiance, wind speed with 98% accuracy

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Solar industry faces some pertinent challenges today. Unpredictable and fluctuating future solar irradiance and wind speed are one of them. The present forecasting services are unable to provide accurate predictions. Thus, in the absence of standard forecasting projections, the renewable energy developers are not able to plan their future production and maintenance in a better way. Furthermore, it adversely impacts the investability and credibility of the existing and upcoming renewable energy projects, reducing their overall share in the energy market. As a result, the renewable utilities have to bear the burden of hefty penalties for not being able to meet their energy commitments.

In our technical research paper, we have attempted to work on a solution to the above problem by considering factors like, the inter-day and intra-day variability of the renewable resources, human or anthropogenic and local environmental factors, Global climatic events, Climate Change and the impact of different factors and the training models (statistical, machine learning, and deep learning). During our research and testing we blended climate science, satellite imagery/data, global meteorological databases, AI, ML, and Deep-Learning to take the predictions to the next level of forecasting accuracy band. 

Finally after careful validation, we developed an AI-based framework to automatically select the best models, factors, and their combinations to optimize the overall accuracy of the predictions. The results were equal to more than 98% accurate. 

To read more about our AI-based framework click on the button below and download our Technical Research paper on ‘Resource Forecasting’. 

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PV Panel analytics

Do you validate your Solar PV Module's Thermal Coefficient?

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PV Panel analytics

A solar PV system is based on a simple mechanism that converts sunlight into energy. However, the process is not as simple as it seems. Some of the incident energy falling on a solar panel is reflected and some other is dissipated as heat, causing the PV module to operate at a higher temperature rather than at ambient temperature. This rise in temperature of the solar PV modules can affect the energy production and overall performance of your solar plant.

The aim of our research was to study the sensitivity of the PV Modules towards rising temperature that negatively impacts the electrical conversion efficiency of a PV module and thus overall performance of the plant.

During our research to determine a PV module’s sensitivity towards the rising PV module temperature, we compared the thermal coefficients specified by the PV module manufacturer in the specification sheet with the thermal coefficients calculated by SmartHelio’s team.

Our team through rigorous research and validation developed a PV Panel Analytics Tool that provides access to real-time sophisticated analytics such as thermal coefficients and PV module degradation to its subscribers, by accessing the real-time raw data with a precision of seconds collected by our IoT devices. Our tool also helps your procurement team to find the most profitable PV module technology for your plant by comparing different technologies for multiple locations.

Read more about our research, Click on the download button below to download our research paper ‘Validating Thermal Coefficients in Outdoor Conditions’

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Ground-breaking Research on Panel-level faults detection

Use of Edge Computing and AI on IoT hardware to identify solar Panel-level faults

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Ground-breaking Research on Panel-level faults detection

Module-level faults can have serious affect on the performance and reliability of any solar PV plant. These faults, if not detected timely, can pose safety hazards like fire accidents on site and eventually lead to significant asset damage and/or under-performance. A better fault detection technology through the use of Edge Computing IoT hardware and machine learning frameworks is the need of the hour. 

A Solar PV system could encounter several faults at module-level. These faults include electrical faults like line to line, line to ground, open circuit or non-electrical faults like glass breakage, bird pooping etc.. In our research we have identified and presented how better and real-time fault detection is possible using cost-effective Edge devices and Machine Learning frameworks. 

The purpose of the research was to identify and develop module-level fault detection frameworks with classification techniques to build low-cost edge-devices (IoT Sensors) that could be deployed at large scale in low-power-output PV arrays. 

Our research classifies the impact of non-electrical faults (glass breakage, delamination, bird poop etc.) on the current and voltage patterns of the panels. This has helped us to detect these non-electrical faults accurately based on their signature patterns and impact on electrical and thermal parameters of PV modules.  

Through the research we were able to classify more than six types of non-electrical module-level faults in a solar PV system with 93% accuracy using the low power edge devices. The research paper gives a clear idea with concrete results on the effectiveness of Machine Learning frameworks and cost-effective Edge Computing for sustainable, profitable and reliable solar PV systems. 

Click on the download button below to download our IoT-based research paper.

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Data Automation tool for solar assets

Clean millions of data points within secs with Data Automation tool

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Data Automation tool for Solar assets

As industries are shifting towards data-driven decisions, It becomes critical to have good quality data to ensure analysis is correct and actionable.

Data not only helps you understand how your solar plant is performing in general  but to compare and analyze how different sections of the plant are performing in comparison to each other using performance indicators like PR, SY, soiling, degradation, etc. For example, data can tell you precisely if a panel is degrading faster than the other panel installed at the same site due to a shadow from the nearby tree. Data can derive intelligence for multiple stakeholders: Sales, engineering, procurement, management and investors. Data intelligence can streamline and give a new way to any solar asset owner to function and manage their company. 

Generally, a MW size of plant produces 100,000 data points in a month. However, as reported by our customers in multiple geographies, 16-20% data points are usually missing in this data due to network or connection failure and/or faulty readings by the data loggers. Because of the huge quantity of data produced otherwise, missing data is usually ignored (an easy way out), thereby creating a void that leads to incorrect conclusions. 

For example, missing data during the sunshine hours can effectively lead to low PR value in days of high energy production, or measurements during night time hours can give a high CUF value which is misleading. 

Also, it is quite common to receive outliers/ bad quality measurements from data loggers, pyranometers due to soiling, bad weather conditions, calibration issues, etc. Generally, as a part of data cleaning, this data is removed from the dataset leading to data loss. 

SmartHelio’s Data Automation Tool aims to provide clean and sanitize data in a single click. 

Our Data Automation tool is designed to clean and sanitize in a way to first remove bad measurements from the dataset and clean the dataset. It later predicts the missing data points with 99% accuracy using Machine Learning algorithms and provides sanitized data. All you need to do is connect your plant via API or upload your data to the tool and click “Submit”. You can simply download sanitized data and use it for your internal analysis. 

 

To know more about our Data Automation tool you can book Ask Me Anything (AMA) session with us every Wednesday & Friday, 1300 CET/ 5.30 PM IST. During the 45 minutes session, you can ask us anything about our tool. So Hurry book you AMA session NOW!

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SmartHelio all set for the Y Combinator Demo Day

SmartHelio all set for the Y Combinator Demo Day

SmartHelio pitching at Y Combinator Demo Day

It was a proud moment for the team when SmartHelio was selected for the Y Combinator startup program W22 batch.  In a few days, on March 29th and 30th, 2022, SmartHelio will be pitching its solution to a league of world-class investors during Y Combinator’s Demo Day.The most awaited event for the startup community all over the world.  The event will feature founders from 43 countries among the 375 startups selected for this batch. 

With the rising concerns of climate change, innovative cleantech solutions are emerging rapidly across the globe. This growth is supported by investors who have invested $775 Bn in cleantech startups in 2021 and it is growing at 27% YoY. It is not only critical to replace fossil fuels but it is also important to make the current and future cleantech infrastructures truly sustainable. 

What does SmartHelio do?

SmartHelio is software that helps solar utilities automatically predict and prevent downtime disruptions, to increase their annual revenue by $10k per MW/ year from their solar plants.

What problem are they solving?

When a solar plant starts underperforming or shuts down suddenly, it directly affects the revenue of solar companies and operational expense increases. The main reason is the inability to automatically identify the reasons. This loss in revenue combined with increased operational expenditure amounts to a $15 Bn/year (and increasing) loss to the industry.

What is SmartHelio’s USP?

Minimize downtime and fix problems with click of a button. Their software reads the live data coming from solar plants, identifies why the plant is under-performing and prescribes actions to the companies with recommended timelines of action. This automation reduces manual interventions in maintaining the solar plants by 80%. If the plant’s data is not accurate, they also offer them their smart sensor IoT device.

Who are their customers?

Solar Developers, often called IPPs – Independent Power Producers (roof-tops and utility).

How did they get here?

The company was the brainchild of Govinda Upadhayay, CEO and Founder of SmartHelio, who is a serial entrepreneur listed in Forbes 30under30 for his work in the clean energy domain. He did his research in climate modeling from a top European tech university, EPFL, Lausanne. Govinda realised that the reactive maintenance of solar plants is affecting the health and its end-of-life. He started his research on designing an intelligent monitoring system that could keep a check on solar assets’ health. 

How has their journey been?

In just two years, SmartHelio bagged 15 international awards for its innovative solution and was selected by 8 prestigious acceleration programs. Recently, SmartHelio won the MassChallenge competition and was selected by the AWS Clean Energy Acceleration program. This year SmartHelio was also awarded the B Corporation Certification for meeting the highest social and environmental standards

About Y Combinator

Y Combinator provides seed funding for startups. Every 6 months over 10,000 companies from across the world apply for the YC accelerator program, commonly called the YC Boot Camp. After critical evaluation by Venture Capitalists, only a selected bunch of companies make it to the three month long Boot Camp. 

During these three months, YC works intensely with startups on their ideas helping founders deal with investors and acquirers. It helps startups to get into the best possible shape and refine their pitch to investors. Each cycle culminates in Demo Day, when the startups present their companies to a carefully selected, invite-only audience.

So far, more than 3,000 companies have been funded by Y Combinator like Airbnb, Coinbase, Dropbox, Reddit, Stripe to name a few. Many of these companies are now Unicorns.   


Dynamic Cleaning Schedule: A data driven cleaning approach

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One of the major roadblocks for obtaining maximum performance of a PV system is “Panel Soiling” caused due environmental and natural factors. Predominantly, deposition of dust, bird droppings, industrial dust, dust storm, snow etc. Such soiling causes reflection, absorption and partial scattering of irradiance which leads to reduced amount of energy that reaches the PV cell. This in turn,

  • Reduces the Energy Yield/Performance Ratio
  •  Increase in O&M cost
  •  Induces more uncertainty in the solar power forecast
  •  Decrease in share of PV energy in the market

The estimated annual losses due to soiling ranges between ~2% and ~6% depending on the geographical location of the PV system. Therefore, an adequate methodology to mitigate soiling losses has become the need of the hour. Unlike other PV system losses, soiling is reversible i.e soiling can be mechanically removed from the surfaces of the solar panels either through natural means such as rain, wind or artificial means such as manual cleaning, semi-automated or fully automated robotic cleaning.

        The most common anti-soiling technique adopted is the manual wet-cleaning of the entire plant as per fixed schedule. Globally, there has been a visible shift towards robotic cleaning adoption. However, 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 labor).

Smarthelio’s Dynamic Cleaning Scheduler takes into consideration soiling trends, meteorological events, cleaning cost and electricity cost and follows an Al-based optimization process to generate a dynamic cleaning schedule and track the cleaning effectiveness for different sections of the plant.

To know more about our Dynamic Cleaning Schedule tool you can book Ask Me Anything (AMA) session with us every Wednesday & Friday, 1300 CET/ 5.30 PM IST. During the 45 minutes session, you can ask us anything about our tool. So Hurry book you AMA session NOW!

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SmartHelio’s HelioCloud GHI Forecast service predicts solar irradiation for any location in the world.

GHI forecasting now includes micro-climatic factors

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Predict solar irradiance with 98% accuracy for any plant across the globe.

SmartHelio’s HelioCloud GHI Forecast service is aimed to assist asset managers, banks and the like to obtain accurate predictions of global horizontal irradiance in any location in the world. 

Global Horizontal Irradiance (GHI), the electromagnetic spectrum obtained from the sun is the direct form of abundant energy resource that fuels many processes on the Earth. Although the sun emits a nearly constant amount of radiation (solar constant = 1361.1 W m-2), we experience considerable temporal and spatial variation in the solar radiation received on the Earth’s surface. Because of this, it becomes important to have a reasonably accurate knowledge on the availability and variability of GHI. 

Today, solar irradiance forecasting receives unprecedented attention from various scientific communities. This is because of the importance of forecasting the variability of solar power for their grid integration, which constitutes a major challenge to a successful transformation of the conventional fossil fuel-based energy sector into a 100% renewable one. 

It turns out, however, that current solutions lack localization of the forecasts. Additionally, such solutions do not also discuss the potential changes in GHI under the umbrella of climate change. But long-term radiation magnitudes recorded over different places proves that GHI undergoes substantial multi-decadal variations, which should be considered in solar resource assessments.

SmartHelio’s HelioCloud GHI Forecast service predicts solar irradiation for any location in the world. To this end, it employs cutting-edge machine learning algorithms along with custom functions to generate insights from historical data that help reduce uncertainty in estimating global horizontal irradiance. 

Furthermore, our algorithms take into account key influencing factors such as:

  • The microclimate of the location (surface roughness, NDVI, aerosol, etc.),
  • Global climatic phenomena (ENSO, NAO, etc.), 
  • Climate change (IPCC RCP Scenarios), and
  • Human factors such as pollution, urbanisation, etc. 

This helps us to achieve long-term forecasting accuracy up to 98%.

We believe that with HelioCloud GHI forecast, solar irradiation forecasting can be reinterpreted from a strenuous task into an effortless and user friendly process whose accuracy can be estimated with greater confidence. 

To know more about our GHI Forecasting tool you can book Ask Me Anything (AMA) session with us every Wednesday & Friday, 1300 CET/ 5.30 PM IST. During the 45 minutes session, you can ask us anything about our tool. So Hurry book you AMA session NOW!

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PV Panel analytics

Your solar assets need panel-level analytics!

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Real-time PV Panel analytics

Enhance your inverter & string-level data insights for solar asset management with real-time PV panel analytics.

 

You might have seen data at a panel-level in real-time, but have you seen panel-level analytics in real-time

As solar companies are expanding their portfolio, there is a pressing need for module-level data for better management of solar PV systems. In such a scenario, only inverter-level data and string-level data is not sufficient for deep data analytics. You need granular level data that can help you diagnose and predict faults in real-time. Our real-time panel level analytics eliminates the need for manual intervention and adds unforeseen value to drone and equipment surveys. Now companies can diagnose and predict the fault at the initial stage of module underperformance. Thus, saving money, time and energy.

Over the past 2 years, our panel-level analytics has helped solar companies to identify and predict PID losses, module degradation, module mismatch, module tilt issues, cell cracks, and many more. Here are a few important use cases, based on our IoT device, that helped our client save more than 30% on O&M expenses and 8% higher energy production. 

  1. Missing inverter data: Using AI and ML algorithms we predict missing inverter data with 99% accuracy. 
  2. Pyranometer data accuracy: Validates the accuracy of pyranometer data. 
  3. PV module degradation rate: Use our root cause analysis to identify, predict and compare faults like cell cracks, soiling pattern, wire losses, hot spots, connector faults etc. 
  4. Compare different PV modules: Compare the performance of different module manufacturers and performance of the modules in different geographies.

Our newly launched PV Panel analytics tool focuses on IoT technology and ML algorithms. We use this technology to provide module-level insights in real-time on any type of solar module. Companies can further enhance their R&D or analytics effort using our module-level raw data,” Sourabh Maladharee, Product Development Head.

PV Panel analyticsTo know more about our PV Panel Analytics tool you can book Ask Me Anything (AMA) session with us every Wednesday & Friday, 1300 CET/ 5.30 PM IST. During the 45 minutes session, you can ask us anything about our tool. So Hurry book you AMA session NOW!

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