The renewable energy sector stands at a pivotal crossroads. As we accelerate toward a sustainable future, digital transformation has emerged as a critical enabler of scale, efficiency, and innovation. Having navigated the renewable landscape across solar and biomass sectors for years, I’ve witnessed both the tremendous promise and sobering reality of digitalization in our industry. Beneath the glossy veneer of technological advancement lies a complex terrain that demands both enthusiasm and critical scrutiny. This transformation journey requires more than adopting new tools; it necessitates a fundamental rethinking of how we operate, innovate, and scale.
The uneven digital landscape
Digital adoption across the renewable energy value chain reveals a stark imbalance. Operations and Maintenance (O&M) has embraced digitalization most enthusiastically, with SCADA systems and IoT-enabled devices revolutionizing efficiency through real-time monitoring and resource optimization. These technologies have transformed O&M from reactive to proactive, significantly reducing downtime and operational costs.
However, stepping beyond O&M reveals a far less impressive reality. Engineering, Procurement, and Construction (EPC) processes remain surprisingly under-digitized despite their critical importance. The truth is that many of our sophisticated dashboards merely replicate manual processes rather than replacing them. Field engineers still wrestle with disconnected workflows, forced to toggle between cutting-edge design software and error-prone spreadsheets for critical decision-making. As one executive pointedly remarked in a recent industry forum, “We’ve spent millions on fancy dashboards that essentially do what our Excel sheets did, just with better graphics.”
Similarly, while we’ve made strides in smart grid technologies and energy trading platforms, interoperability issues continue to plague the industry. The promise of seamless integration remains unfulfilled as systems from different vendors struggle to communicate effectively, creating digital silos instead of the connected ecosystem we envisioned.
AI: The promise vs. reality
Artificial intelligence represents a cornerstone of digital transformation in renewable energy. The vision was compelling—AI would analyze vast datasets to predict energy production, optimize grid integration, and prevent equipment failures before they occurred. Yet the reality has been sobering. While some applications have shown promise, many AI implementations have delivered marginal improvements at best.
Predictive maintenance, for instance, was supposed to revolutionize asset management by detecting failures before they occur. However, many operators have found that the benefits often fail to justify the substantial investments required. Models frequently generate false positives, sending maintenance teams on costly wild goose chases, or miss critical issues entirely.
The energy forecasting domain tells a similarly disappointing story. Despite years of advancement in machine learning techniques, the accuracy of renewable energy production forecasts remains frustratingly limited for longer time horizons. One wind farm developer recently confided that their multi-million-dollar AI forecasting system was consistently outperformed by their meteorologist’s expert judgment during critical weather events.
The hidden implementation challenges
The gap between digital transformation’s potential and its practical impact stems from several interconnected challenges that receive insufficient attention in industry discussions.
Data quality remains a persistent obstacle, particularly for AI applications. Renewable energy operations often generate data that is incomplete, inconsistent, or simply inadequate. Sensors fail, communications drop, and standardization across diverse assets is minimal. Consequently, data scientists spend the vast majority of their time cleaning and preprocessing data rather than developing innovative solutions.
Integration with existing infrastructure presents formidable hurdles. Many renewable energy companies operate through multiple Special Purpose Vehicles (SPVs), each with unique financial and operational systems. When a company manages dozens of entities with distinct workflows, the challenge of digital integration becomes exponentially more difficult.
Perhaps most critically, we’ve underestimated the human dimension of digital transformation. Engineers accustomed to traditional methods may resist app-based updates or automated processes not because they’re resistant to innovation, but because these tools often fail to account for on-ground realities. As one field engineer bluntly stated, “The app they want us to use takes three times longer than our current process and doesn’t even work offline where most of our sites are located.”
Reimagining digital strategy in renewable energy
Moving forward requires a more nuanced approach to digitalization—one that balances ambition with pragmatism. First, we must recognize that digital transformation cannot be implemented piecemeal. The “just do it” approach advocated by some technology enthusiasts fails to appreciate the complexity of energy systems. Instead, companies need comprehensive strategies that address not just technology but also people, processes, and organizational structure.
Second, we must be more critical about the solutions we adopt. This requires involving frontline workers in the design process and focusing on solving real problems rather than deploying technology for its own sake. For many organizations, this might mean beginning with basic automation and analytics before advancing to more sophisticated AI applications.
Third, we must invest significantly more in building digital-ready workforces. The most sophisticated technology is worthless without people who understand how to use it effectively. This means not just technical training but fostering a culture that embraces continuous learning and adaptation.
The path forward
Despite these challenges, I remain genuinely optimistic about digital transformation’s potential in renewable energy. AI-driven forecasting can optimize energy production based on weather conditions and demand patterns. Decentralized energy models like virtual power plants can enhance resilience while reducing dependence on centralized grids. Advanced analytics can transform vast amounts of data into actionable insights that drive efficiency and reduce costs.
The key lies in approaching transformation with both ambition and clear-eyed realism. By acknowledging the gap between promise and reality, we can chart a more effective course—one that harnesses technology’s potential while recognizing its limitations. This means moving beyond superficial digitalization to fundamentally reimagine how we generate, distribute, and consume clean energy.
The future of renewable energy will unquestionably be digital, but that future will arrive through evolution, not revolution. With pragmatic implementation, genuine attention to human factors, and patient investment in integrated systems, we can fulfill the true promise of digital transformation: accelerating the renewable energy transition while creating more efficient, resilient, and sustainable energy systems for generations to come.
The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.
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