Meet SmartHelio Team at REI-Expo 2022

Meet SmartHelio team at REI-Expo-2022

Meet SmartHelio Team at REI-Expo 2022

It will be the first-ever event in India, where our team will be giving a LIVE Demonstration of our predictive analytics straight from our test facility. The entire team is excited to showcase our predictive analytics solution, which has already gained traction with major solar developers in the EU and USA. 

For the first-time SmartHelio is exhibiting at Asia’s leading renewable energy event- REI-Expo-2022 going to be held at India Expo Centre, Greater Noida from Sept 28-30th. Our teams from the USA, Europe, and India are visiting India specially for the event where we will be giving a LIVE Demonstration of our predictive analytics straight from our test facility. All this in front of our booth visitors.  After getting huge responses at ISE Germany and RE+ USA, SmartHelio is exhibiting at REI-Expo, Asia’s largest renewable energy event. 

It will be the first-ever event in India, where all our team members including our Data Scientists, PV experts, Resource forecasting and climate modeling scientists, and IoT experts will be present. The entire team is excited to demonstrate LIVE, our predictive analytics solution, which has already gained traction with major solar developers in Europe and the USA.

As the cost of solar panels has dropped, so has the economic support for it, in the form of subsidies. The decline in the cost of solar has led to a lower bidding tariff, year after year. With prices dropping as much as 50 to 80% from 2015 to 2018, the profit margin for solar developers has thinned proportionately. Stuck in a classical case of increased merchant risk, in upcoming years, developers will be forced to trim their expenses on operational activities. Therefore, the need for digitalization and advanced analytics for optimal decision-making will become the priority not only for the developers but also for the financiers. 

To support the global expansion of solar PV, solar financers rely heavily on the accuracy of probabilistic scenarios (e.g., P50, P90, P99 estimates) to structure the terms of energy deals and identify appropriate risk mitigation strategies. Inaccurate estimates significantly increase the risk of default on solar loans as an asset class, meaning the project has the insufficient operating cash flow to meet its debt service obligations. 

Last year’s 2020 Solar Generation Index (SGI) report revealed that solar projects are on average underperforming their target production (P50) estimates by 6.3%, which can amount to a loss of USD 3000 per MW. Allowing these risks to go unchecked harms the investment returns and ultimately damages the industry’s collective credibility. It is now more important than ever for financers, sponsors, insurers, consultants, lawyers, and engineers to reflect on our current trajectory and to build new solutions to manage these emergent risks. Over the past few years, the importance of O&M has also penetrated the financial sector and investors have started to demand confidence that their investment would be safe for the long term. This has further enabled the fast development and deployment of advanced APM solutions like SmartHelio. 

SmartHelio has developed a profile-agnostic analytics & automation software for solar photovoltaic assets that can generate diagnostic, prognostic, and prescriptive insights for the solar PV stakeholders (O&M Teams, Asset & Portfolio Managers, CXOs, Investors, and OEMs) in real-time. SmartHelio is deploying AI on Edge devices to reduce the cost of data collection, storage, and processing without compromising the quality of analytics services. 

This will empower energy developers and managers to maintain their renewable energy assets consistently at a high-performance level with minimum human intervention. Early adopters in Europe have proven that our technology is well suited to Northern installations, and our experience in India shows that we are especially appropriate for sun-wealthy countries that will be striving to expand their energy infrastructure as part of their development and carbon mitigation strategies for the upcoming decades. 

In the future and with its ongoing efforts, we as a company want to establish a digitalized, federated, democratized, and distributed computational ecosystem in the energy industry. At REI-Expo, we will be showcasing some of our capabilities through LIVE demonstrations of our powerful analytics and automation services. Therefore, we invite all clean-tech enthusiasts and visionaries to visit our booth at the mega event in India.

Book your slot for LIVE Demo of predictive analytics by SmartHelio at REI-Expo

Decoding Digital Twin for solar plants

Decoding Digital Twin for Solar Plants

Decoding Digital Twin for solar plants
A Digital Twin looks-like, behaves-like, and connects-to a PV solar plant system with the goal of improving or optimising decision making processes.

Investment in the solar PV energy projects have increased in recent years and so has the use of advanced solutions for optimizing solar operations and maintenance like Digital Twin, edge computing, and IoT. We are decoding the concept of Digital Twin in this article and explain to you how this technology is helping to transform the way PV solar plants function and operate.

With the use of advanced IoT solutions and smart sensors, now we can get PV module-level data insights on any kind of solar PV asset. Detailed analysis of this data is used to design advanced solutions like Digital Twin.

Effective use of Digital Twin can help solar asset managers detect early patterns of system underperformance, which later can be used to predict potential faults, quantify the impact of existing/upcoming faults or losses. Thus, Digital Twinning techniques help solar developers to anticipate PV system’s future production, to plan predictive and corrective maintenance activities, and to improve bankability of future projects.

1. What is a PV plant Digital Twin?

A Digital Twin is a virtual model of a real system. While changing the input variables, a Digital twin helps to conduct experiments and to test hypothesis, as well as to predict the behavior of the system and to manage its life cycle. A Digital Twin also helps to prevent equipment failures, poor system design and minimizes operating costs. In the specific case of the solar PV industry, a Digital Twin is a digital copy of the PV plant, including all relevant design and location information, as well as information from the past; including major losses causes and performance indicators breakdown.

3. Why do Asset Managers need Digital Twin?

Digital Twin is a faster way of comparing, at specific conditions, PV plant’s current performance with its expected performance drawn from its digital copy. After a Digital Twin of a Solar PV Plant is created, the only required inputs, other than real-time plant data, are irradiance and temperature. Therefore, performance and losses analysis becomes faster and easier to perform. More advanced solar PV plant Digital Twins even include a parameters’ breakdown and trends to indicate root causes of faults or losses in real-time. Thus, making daily tasks easier for Asset Managers and increase the uptime of the PV solar plants.

Understanding Digital Twin for solar plants

2. How do you make a Digital Twin?

A Digital Twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the PV solar power system’s life cycle. A Digital Twin looks-like, behaves-like, and connects-to a PV solar plant system with the goal of improving or optimising decision making for any time horizon. It should be localized by considering the geographical position of a PV plant, locally measured power and weather data, and power loss factors.

An ideal Digital Twin should be self-adaptive, that means, it should be able to modify and recalibrate its behaviour and likeness under changing operating and external conditions, such that it satisfies operational goals and constraints to accommodate possible future operational uncertainties.

Step by step process of how to create a Digital Twin for solar plants

3. How SmartHelio uses Digital Twin?

In general terms, creating a Digital Twin is a faster way to detect even the smallest of  deviations in performance and make sense out of different trends within the historical data. At SmartHelio, we create and localize Digital Twins of PV plants by adding design factors, geographical factors, and performance factors and trends. Our Digital Twin models automatically prioritizes maintenance activities to minimize O&M costs and maximize solar PV  plant’s uptime. It’s a blend of advanced technology, continuous learning with a custom tailored understanding of O&M costs and resources for each plant or company. Our Digital Twins do what a Digital Twin is actually supposed to do- directly derive actionable insights!

Authored by: Dorian Guzman  Co-authored by: Govinda Upadhyay, Shankaransh Srivastava

Planning to set up a Digital Twin for your solar PV plant?

Learn how our team can help you. Click on the button below to talk to our solar energy experts.

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SmartHelio among 'Best for the World' companies labelled by B Lab.

'Best for the World' recognition by B Lab

SmartHelio among 'Best for the World' companies labelled by B Lab.SmartHelio has been recognized as “Best for the World” by B Lab and topped the Environment category in Switzerland. Read to know more…

Deep-data analytics company SmartHelio, was recently recognised as “Best for the World” by B Lab. B Lab in its annual B Impact Assessment evaluated top-performing B Corps on five categories-community, customers, environment, governance, and workers. SmartHelio, with a score of 116.8, was recognised as “Best for the World” in the environment category.

“SmartHelio is a mission driven company and the impact on the environment is core to our daily operations. We are delighted that our efforts are being acknowledged by B Corp. We hope that more organizations will join this movement,” Govinda Upadhyay, CEO & Founder.

SmartHelio is among top three Swiss cleantech companies to be selected as ‘Best for the World’  in the Environment category. The company has excelled in environmental performance by offering advanced and effective digitization, AI and automation practices for effortless solar plant performance management and predictive maintenance. Our solutions make clean energy asset management and energy operations profitable and long-lasting.

What is ‘Best for the World’ recognition by B Lab? 

Every year, B Lab recognizes the top-performing certified B Corporations creating great impact through their businesses. Once their verified scores in the five impact areas evaluated on the B Impact Assessment – community, customers, environment, governance, and workers – are amongst the global top 5% in their corresponding size group, these B Corps are named Best for the World.

Why is the B Lab ‘Best for the World’ recognition important?

The “Best for the World” recognition is important in the sense that chosen B Corps in the five different categories prove that the competing organisations are not only the best in the world, but more importantly the best for the world. Their actions inspire more businesses to join the movement that is transforming the global economy to benefit all people, communities, and the planet.

To achieve B Corp certification, a company must meet these rigorous criteria:

  • Demonstrate high social and environmental performance by achieving a B Impact Assessment score of 80 or above and passing our risk review
  • Make a legal commitment by changing their corporate governance structure to be accountable to all stakeholders, not just shareholders, and achieve benefit corporation status if available in their jurisdiction.
  • Exhibit transparency by allowing information about their performance measured against B Lab’s standards to be publicly available on their B Corp profile on B Lab’s website. 

“We have always kept climate-conscious innovation at the core of our company. In our technological offerings or in our business models, we have institutionalized certain measures where we always assess our performance in terms of contributions made to the climate or environment. This approach has truly helped SmartHelio to be among the global front-runners in climate and environmental sustainability,” Neeraj Kumar Dasila, Co-Founder & Chief Technology Officer, SmartHelio.

Learn more about B Corp Certification

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 - Empowering solar asset managers

Inefficiencies related to solar PV plant management and O&M can be handled using effective digitization, Artificial Intelligence, and automation practices. Read to know how predictive maintenance can empower solar asset managers

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 a great news but contrarily, it is also making the solar 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 solar 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 related to solar plant’s operation and maintenance can be summarized below:

  1. Unable to detect the early signs of equipment failure or faults in the solar PV system which leads to system under-performance 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 solar 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 clean energy production and revenue. In most cases, the solar plant 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 solar asset management and O&M can be handled by introducing effective digitization, Artificial Intelligence, and automation practices. In our efforts, we have introduced An Autopilot for the solar PV industry. The Autopilot works as a virtual support assistance to the Asset Managers, Performance Managers, and Plant Managers. 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 solar 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 using 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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Can advance forecasting predict solar irradiance, wind speed with 98% accuracy?

Talk to our experts to know more about advance GHI Forecasting

This technical research paper by SmartHelio focuses on how to effectively combine climate science, satellite imagery/data, global meteorological databases, Artificial Intelligence, Machine Learning, and Deep-Learning to improve the accuracy of solar and wind resource forecasting. Read to know more.

Solar PV plants face some pertinent challenges today. Unpredictable and fluctuating future solar irradiance and wind speed are one of them. The present solar and wind forecasting services are unable to provide accurate predictions. Thus, in the absence of standard forecasting projections, renewable energy developers are not able to plan the future production, operations and maintenance of clean energy assets 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 clean energy market. As a result,  renewable energy utilities have to bear the burden of hefty penalties for not being able to meet their energy commitments.

In our technical research paper on advance GHI Forecasting, 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 energy 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’. 

Download Research Paper

PV Panel analytics

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

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PV Panel analytics

This technical research paper highlights sensitivity of the PV Modules towards rising temperature that negatively impacts the electrical conversion efficiency of a solar PV module and thus overall performance of the plant. Read to know more.

A solar PV plant system is based on a simple mechanism that converts sunlight into clean energy. However, the process is not as simple as it seems. Some of the incident energy falling on a solar PV panels is reflected and some other is dissipated as heat, causing the solar 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 power 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’

Download Research Paper

Ground-breaking Research on Panel-level faults detection

How to identify panel-level faults in solar plants using AI based Edge Computing and IoT hardwares

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Ground-breaking Research on Panel-level faults detection

Identifying and localising panel-level faults in solar plants is a challenge. Read our technical research paper to find out how better and real-time fault detection is possible using cost-effective Edge computing IoT devices and Machine Learning frameworks.

Solar PV 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 solar asset damage and/or under-performance. A better fault detection technology combined with Edge Computing IoT hardware and Machine Learning frameworks is the need of the hour. 

A Solar power PV plant 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 solar 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 plants

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 AI-based Data Automation for solar plants

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Data Automation tool for Solar assets

Missing data and poor data quality can have serious impact on your solar analytics and overall performance of your solar PV plant. Read to know how you can solve this issue using AI-based Data Automation technology.

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

What is the role of data in solar PV plant’s performance?

Data not only helps you understand how your solar PV plant is performing in general but also helps to compare and analyze how different sections of the solar 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. In short, data intelligence empowers a solar company to take informed decisions and plan their solar O&M in a better way. 

The curious case of missing solar data points 

Generally, a MW size of a solar PV 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. 

AI-based Data Automation tool for any kind of solar PV plant

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 our solar experts. 

Read More…

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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

World’s fastest growing clean-tech company SmartHelio is now backed by Y Combinator. SmartHelio will be pitching its solution to a league of world-class investors during Y Combinator’s Demo Day. Read to know more…

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 provides advanced predictive analytics and IoT solutions that help 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 buttonTheir 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. Thus, turning reactive maintenance to predictive maintenance for solar PV plants.  

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.