Solar Energy Index rose to 40 USD on August 15, 2025, up 9.08% from the previous day. Over the past month, Solar Energy Index's price has risen 5.60%, but it is still 1.84% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Solar Energy Index.

Prices for Solar Energy Index including live quotes, historical charts and news. Solar Energy Index was last updated by Trading Economics this July 27 of 2025.

This statistic represents the market share of leading commercial solar installers in the United States in 2011. With a national market share of nine percent, California-based SunPower was ranked first that year.

The global solar energy market was valued at around USD 53 billion in 2018 and is estimated to reach around USD 224 billion with a CAGR of nearly 20.5% over the forecast period, 2021-2028. Solar energy consists of energy radiated from the sun. With enhancements in technology, solar energy has emerged as an efficient and convenient form of unconventional energy.



Photovoltaics and concentrated solar power systems are the technologies used to harness solar energy. They are used to combat greenhouse emissions and global warming. Solar energy is widely available and promoted in all developing and developed countries, which has positively influenced the market.

Market Trends, Drivers, Restraints, and Opportunities:

• The development of solar energy market is driven by increasing government incentives and tax rebates, followed by increasing demand for electricity in industrial sector, due to rise in population and infrastructure.


• Climate change has fueled the demand for protection of environment. Conventional energy systems use fossil fuels, which experience fluctuating prices and lead to pollution. Solar energy, which is a renewable source of energy, generates power and may replace conventional energy systems. This factor is anticipated to favor the market growth. Increasing investments, R&D, and incentives in solar energy drive the growth of the solar energy market. For example, China has planned to provide solar Feed in Tariff (FIT) payments to large-scale projects.


• The initial prices of installing solar panels and silver, which is used as a component, are high. This is expected to hinder the market growth. In addition to this, limitation in land available due to technical problems and ecological concerns are estimated to hamper the market growth. Countries like Russia, which have unfavorable or cold climatic conditions, restrain the growth of the solar energy market.


• Advancements in solar photovoltaic systems are estimated to increase the implementation of storage grids, hence leading to increased opportunities in the solar energy market.

Scope of Solar Ener

U.s. solar power market valued USD 53.45 Billion in 2024 and is projected to surpass USD 123.86 Billion through 2032

Solar energy market size and share predicted to reach USD 540.3 Billion by 2034, with a CAGR of 7.2% during the forecast period. Increasing government initiatives driving the solar energy industry growth.

The North America Solar Photovoltaic (PV) Market is segmented by Application (Residential, Commercial, and Utility), Deployment (Ground Mounted and Rooftop Solar), Technology (Crystalline Solar and Thin Film), and Geography (United States, Canada, and Mexico). The report offers the market size and forecasts installed capacity (GW) for all the above segments.

This statistic shows the market share of the U.S. photovoltaic cell and module production in 2010. Arizona-based manufacturing company First Solar had a market share of around 20.2 percent in this period.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Sunnova Energy (SUNE): Solar Power to Profits?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

According to Cognitive Market Research, the global Solar Energy market size will be USD 95451.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 6.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 38180.6 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 28635.4 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 21953.8 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 4772.5 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 1909.0 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
The Photovoltaic Systems Technology held the highest Solar Energy market revenue share in 2024.

Market Dynamics of Soloar Energy Market

Key Drivers of Soloar Energy Market

Increase in energy demand to Increase the Demand Globally: The growth of the global solar energy market is primarily driven by the increasing energy demand due to a surge in population. As the global population continues to rise, especially in developing countries, the energy demand grows proportionally. Urbanization is also accelerating, with more people moving to cities, leading to greater energy needs across residential, commercial, and industrial sectors. This rising energy demand is coupled with a growing emphasis on sustainable solutions due to environmental concerns.

Countries Aiming to Achieve Green Energy Targets to Propel Market Growth: A global energy transition is urgently required to limit the increase in average global surface temperature to below 2°C. Consequently, the installation of renewable energy sources is expected to grow significantly in the coming years, driving market expansion. The shift from fossil fuels to low-carbon solutions will be crucial, as energy-related carbon dioxide emissions account for two-thirds of all greenhouse gases. Government initiatives and new energy targets aimed at promoting sustainable energy have positively influenced market growth.

Key Restraint of Soloar Energy Market

High Investment and Lack of Infrastructure to Limit the Sales: The overall cost of solar PV systems is higher than that of traditional solar panels, which may limit their adoption in residential buildings with comparatively lower energy needs. For instance, installing 15 ground-mounted solar panels with a capacity of 300 watts each would cost approximately USD 14,625, with an additional USD 500 per panel for the mounting structure. This higher initial cost can lead to reduced utilization of solar power generation systems. Additionally, inadequate infrastructure further restricts investments in the market.

Trends in Soloar Energy Market

Rapid Adoption of Floating Solar (Floatovoltaics): With limited land availability and rising energy demand, floating solar farms are gaining traction—especially in countries like China, India, and Japan. These systems are installed on lakes, reservoirs, and irrigation ponds, reducing land use while benefiting from natural cooling that boosts efficiency. They also help diminish water evaporation and algae growth, offering both energy and environmental advantages. As deployment costs decline and performance improves, floating solar is emerging as a fast-growing segment of utility-scale solar energy.

Integration of Solar + Storage and Energy-as-a-Service Models: The solar sector is shifting toward integrated solutions that pair photovoltaic systems with battery storage and smart energy management services. This enables consumers and businesses to optimize energy use, store excess production, and gain grid independence. Energy-as-a-Service (EaaS) models—where solar-plus-storage setups are offered on a subscription basis—are becoming popular in commercial and industrial markets. These models reduce upfront costs and simplify system adoption, accelerating distributed s...

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(DQ) DAQO Solar Power: Shining Bright or Fading Fast?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

According to Cognitive Market Research, the Global Solar Panel market size will be USD 171548.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 68619.2 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 51464.4 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 39456.0 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 8577.4 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 3430.9 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Photovoltaic Systems Technology held the highest Solar Panel market revenue share in 2024.

Market Dynamics of Solar Panel Market

Key Drivers for Solar Panel Market

Investments in Renewable Energy to Increase the Demand Globally

One of the primary factors driving the growth of the solar panel market is the increasing global investment in renewable energy. These investments are rising due to the cost-effectiveness of renewable energy production and its low carbon emissions. In the United States, renewable energy is projected to account for 42% of electricity generation by 2050, a significant increase from the current 20%. Between 2000 and 2020, utility-scale electricity generation from renewables in the U.S. grew by roughly 120%, rising from 356 billion kilowatt-hours (kWh) to 783 billion kWh. In 2020 alone, renewables contributed 19.5% of the nation’s net electricity generation. This surge in renewable energy investment directly supports the expansion of the solar panel market. As governments and private sectors increasingly focus on transitioning to clean energy, solar power—being one of the most scalable and widely available sources—becomes a key focus for development. The significant growth in renewable energy generation not only indicates a favorable environment for solar investments but also drives technological advancements, economies of scale, and supportive policies that further accelerate the adoption of solar panels. This, in turn, fuels the solar panel market’s expansion, making it a cornerstone of the global shift toward sustainable energy. https://www.trade.gov/sites/default/files/2022-04/2022SelectUSARenewableEnergyGuide.pdf

Growing Demand for Electric Vehicles (EVs) to Propel Market Growth

Rapid urbanization and infrastructure expansion in emerging economies are driving the demand for energy. Globally, an increasing share of the population is residing in cities. In 2012, 52.5% of the population lived in urban areas, a figure that was projected to rise to 56.9% by 2022. This percentage is generally higher in developed regions (79.7% in 2022) compared to developing areas (52.3%). In Least Developed Countries (LDCs), urban residents remain a minority at 35.8%. In the U.S., urban population growth was notable, with a 6.4% increase between 2010 and 2020 according to the 2020 Census data. As urban populations grow, new construction projects are increasingly incorporating solar energy solutions due to building regulations, energy efficiency requirements, and the push for sustainable urban development. Rooftop solar installations are becoming more common in both residential and commercial buildings, reflecting the growing emphasis on clean energy in urban planning and development. https://hbs.unctad.org/total-and-urban-population/ https://www.census.gov/newsroom/press-releases/2022/urban-rural-populations.html

Key Restraint for the Solar Panel Market

High Initial Installation Costs to Hamper the Market Growth 

The high initial installation costs continue to be a major barrier to the growth of the global solar panel market. While the price of solar panels has significantly dropped over the years, the overall upfront investment required for a complete solar sys...

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

First Solar (FSLR) Stock: Shining Bright or Fading Away?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Foresight Solar Stock Forecast: Shining Brighter Than Ever (FSFL)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This statistic displays the market share of the solar power industry worldwide in 2018, broken down by region. As of 2018, China accounted for ** percent of the world's solar market. This figures provides an estimate for global solar power on-grid installations.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

( ARRY ) Array Technologies: Solar Power's Bright Future?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The report covers Solar Energy Companies in Switzerland and the market is segmented by Type (Solar Photovoltaic and Concentrated Solar Power) and Location of Deployment (Residential and Commercial & Industrial (C&I) and Utility-scale).

Composite Solar Back Sheet Market Outlook

The global composite solar back sheet market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This market is witnessing substantial growth primarily due to the increasing adoption of solar energy systems across various sectors, driven by the pressing need for renewable energy sources and advancements in solar technology.

One of the primary growth factors for the composite solar back sheet market is the global push towards renewable energy. Governments and organizations worldwide are implementing regulations and incentive programs to support the adoption of solar energy. This has significantly increased the demand for solar panels, and consequently, the demand for high-quality back sheets that enhance the durability and efficiency of these panels. Furthermore, technological advancements in composite materials have led to the development of back sheets with superior properties, such as higher resistance to environmental degradation, which is another factor driving market growth.

Another significant growth factor is the increasing investments in the solar energy sector. Corporate entities and investors are recognizing the potential of solar energy to mitigate climate change and reduce carbon footprints. This influx of capital has led to the expansion of solar installations, both in residential and commercial segments. As a result, the demand for composite solar back sheets, which play a crucial role in the longevity and performance of solar panels, is on the rise. Additionally, the growing awareness among consumers about the benefits of solar energy, such as cost savings on electricity bills and environmental sustainability, is further propelling market growth.

The market is also benefitting from advancements in manufacturing processes and materials used for composite solar back sheets. Innovations in polymer technology and the use of nanomaterials have resulted in back sheets that offer better protection against UV radiation, moisture, and mechanical stress. These improvements not only enhance the lifespan of solar panels but also improve their overall efficiency. Consequently, manufacturers are increasingly adopting these advanced materials, which is expected to drive the growth of the composite solar back sheet market during the forecast period.

The introduction of Solar PV Backsheets has been a game-changer in the solar industry. These back sheets are specifically designed to protect photovoltaic modules from environmental stressors, thereby enhancing their durability and performance. Solar PV Backsheets act as a critical component in the solar panel assembly, providing electrical insulation and mechanical protection. They are engineered to withstand harsh environmental conditions, including UV radiation, moisture, and temperature fluctuations, which are common challenges faced by solar installations. By ensuring the longevity and efficiency of solar panels, Solar PV Backsheets contribute significantly to the overall reliability and sustainability of solar energy systems.

In terms of regional outlook, Asia Pacific is expected to dominate the composite solar back sheet market during the forecast period. The region's rapid economic growth, coupled with increasing energy consumption and supportive government policies, is driving the adoption of solar energy. Countries like China, India, and Japan are investing heavily in solar infrastructure, which is boosting the demand for composite solar back sheets. North America and Europe are also significant markets, driven by stringent environmental regulations and strong governmental support for renewable energy projects. Other regions, such as Latin America and the Middle East & Africa, are witnessing gradual growth due to increasing awareness and investment in solar energy solutions.

Material Type Analysis

The composite solar back sheet market is segmented by material type into fluoropolymer and non-fluoropolymer. Fluoropolymer back sheets are known for their excellent resistance to UV radiation, chemicals, and environmental degradation. These properties make them highly suitable for use in harsh outdoor conditions, thereby extending the lifespan of solar panels. As a result, fluoropolymer back sheets are widely used in both residential and commercial solar installations. The high demand for durable and reliable back sheets in t

The global solar energy market size reached nearly 205.13 Gigawatt in 2024. The market is assessed to grow at a CAGR of 7.70% between 2025 and 2034, reaching around 430.71 Gigawatt by 2034.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Canadian Solar Shares (CSIQ): Worth Buying?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data