Widecons is an innovative startup with a mission to support SMEs in digital and green transformation. At Widecons, we firmly believe that these two transformations are deeply connected and synergistic. Over the years, the integration of digital technologies with green solutions has created fundamental new opportunities to improve efficiency and sustainability. One of the best examples of this synergy is the development of digitally regulated photovoltaic systems.
These systems combine real-time data analytics, IoT sensors, and artificial intelligence-based optimization algorithms to dynamically adjust energy production and consumption, leading to greater efficiency, waste reduction, and integration with smart grids. This approach not only maximizes yield, but also ensures a sustainable and forward-looking energy strategy for businesses.
Principle of PV operation, costs and market trends
The heart of a photovoltaic (PV) system is the photovoltaic cell, which is capable of directly converting solar radiation energy into electrical energy. No thermodynamic conversion (heat to work) is involved in this process, although thermal conditions affect the PV system’s electricity production. The cell acts as a battery (or rather, a current generator) capable of supplying 3-4 amps (at about 0.5V) when the receiving surface is 0.01m² and the irradiance is 1 kW/m².
Cells are connected in series to form PV modules, which provide direct current at voltages of tens of volts. The inverter and other electronic components control the output of the PV system and transform the direct current into alternating current, suitable for connection to the grid.
Photovoltaic systems have fairly significant investment costs, but offer proven reliability and long service life.
As of 2024, the cost of PV modules will be around €1.00 to €1.20/Wp, due to reduced material costs and optimized technologies. Thus, the total turnkey cost of a small-scale PV system can range between €1,200 and €1,500/kWp. This estimate varies depending on the location and specifications of the system and reflects the continued decline in prices from previous years.
Wp (Watt peak) is the unit of measurement that indicates the maximum power a PV panel can generate under ideal conditions of exposure to sunlight. It represents the nominal capacity of the module and is used to estimate the power output of the system.
Global cumulative global PV capacity has grown to 1.6 TW in 2023, up from 1.2 TW in 2022, an increase from 407.3 GW to 446 GW of new PV systems installed. About 150 GW of additional capacity comes from photovoltaic modules stored around the world. After several years of rising material and transportation costs, module prices have plummeted.
This dramatic decrease, mainly due to oversupply, has allowed PV to remain competitive, even as electricity prices have fallen after historic peaks in 2022, while supporting the transition to cleaner energy sources
Europe continued to show strong growth by installing 61 GW (including 55.8 GW in the EU), driven by a recovery in Germany (+14.3 GW), and increasing volumes in Poland (+6.0 GW), Italy (+5.3 GW) and the Netherlands (+4.2 GW), while Spain experienced a slight decrease (-7.7 GW) (source: IEA-PVPS report T1-42: 2024).
Digital technologies such as IoT, cloud computing, artificial intelligence (AI), and machine learning (ML) significantly complement and optimize PV systems, especially for B2B applications and industrial customers. Here are 10 reasons why these technologies are strongly synergistic with PV systems, in descending order of importance
1- Optimization of energy production (AI & ML).
- Technologies involved
The combined use of reinforcement learning (RL) and deep neural networks (DNNs) for solar panel orientation and inverter settings improves energy efficiency by adapting to external variations such as irradiance and temperature. However, since automatic panel orientation requires a high investment and maintenance cost, in the short term, optimizing inverters with AI/ML is the most affordable solution. This approach allows companies to improve performance without the high cost of tracking systems, while solar tracking will become more widespread as technologies evolve.
- Quantified benefits
Based on ML models that predict the best angle and time for energy harvesting, weather forecasts, and energy demand trends, optimization through AI can improve energy yield by 10-15%
2- Predictive maintenance (AI & IoT sensors)
- Technologies involved
IoT sensors continuously collect key plant data (voltage, current, temperature), while anomaly detection algorithms (e.g., Random Forest, Decision Trees) predict component failures.
- Quantified benefits
Because early failure detection allows action to be taken before major malfunctions occur, predictive maintenance reduces downtime by 30-50% and maintenance costs by 20-30%.
3- Real-time monitoring (IoT & cloud integration)
- Technologies involved
IoT-enabled sensors send data to a cloud platform that, through dashboards and alerts, provides real-time analysis of system performance. It is common practice to use technologies such as MQTT or LoRaWAN for data transmission.
- Quantified benefits
Because the data collected enables rapid adjustments and detection of inefficiencies, the real-time monitoring can increase operational efficiency by up to +20%.
4- Integration with smart power grid (IoT & distributed AI)
- Technologies involved
IoT devices connected to smart grids use AI algorithms, such as distributed energy resource management (DERM) systems, to optimize the transfer of energy between producers and consumers.
- Quantified benefits
Integration with smart grids improves power distribution efficiency by 10-20%, reducing peak demands, providing greater grid stability, and minimizing energy losses.
5- Energy load management (AI-based optimization)
- Technologies involved
Machine learning algorithms (e.g., Long Short-Term Memory networks [LSTM]) analyze consumption patterns and PV production, balancing supply and demand in real time.
- Quantified benefits
Advanced load management reduces energy costs by up to 15 percent and increases solar energy self-consumption by 20 percent, reducing dependence on external sources.
6- Advanced data analysis (cloud-based AI analysis)
- Technologies involved
Cloud platforms such as AWS or Azure run artificial intelligence algorithms (e.g., k-means clustering for trend analysis) on large datasets generated by PV systems, providing detailed insights into performance and any anomalies.
- Quantified benefits
Data analysis improves PV system efficiency by 10-12% by optimizing activities and identifying opportunities for further energy savings.
7- Supply and demand forecasting (AI/ML algorithms)
- Technologies involved
To ensure fully efficient system operation, ML models, such as recurrent neural networks (RNNs) or Support Vector Machines (SVM) algorithms, predict energy production based on weather forecasts and historical data.
- Quantified benefits
Accurate forecasts reduce energy waste by up to 10-15% by improving planning of energy use and storage, especially during peak production periods.
8- Automation of energy management (IoT, AI and SCADA systems)
- Technologies involved
Control and data acquisition (SCADA) systems, combined with artificial intelligence, automate inverter regulation, battery management, and solar panel adjustments.
- Quantified benefits
Automation improves response time to changes in production or energy consumption, increasing system efficiency by 10-15% and reducing human intervention costs.
9- Reducing operational costs (AI and cloud)
- Technologies involved
Cloud platforms enable AI-based automation, eliminating the need for manual monitoring and intervention. AI optimizes inverter settings and energy allocation, based on real-time data.
- Quantified benefits
Reducing the need for manpower lowers the operating costs of PV systems by up to 20 percent, particularly by minimizing downtime and improving system reliability.
10- Sustainability and reporting (AI & IoT for data collection)
- Technologies involved
IoT-enabled PV systems collect data processed by cloud-based AI platforms to automatically generate sustainability reports that meet compliance standards.
- Quantified benefits
Companies reduce reporting costs by 10-15% by automating the generation of compliance reports, while improving the ability to achieve sustainability goals through more accurate data.
In summary, the integration of digital technologies into PV systems ensures both increased efficiency and optimized performance, as well as considerable cost reduction and increased system reliability.