The review is notable for its scale and rigor. The team screened the literature from 2015 to 2025 using structured searches across IEEE Xplore, Web of Science, and Scopus, with a targeted update in December 2025, and ultimately analyzed 130 peer-reviewed studies following the PRISMA 2020 reporting guidelines. Rather than treating forecasting, optimization, and energy storage management as separate silos—the way most previous surveys have done—the authors deliberately examined all three pillars together. Their central insight is that these components form a decision pipeline: predictions feed optimization, optimization feeds storage control, and storage control feeds back into the physical system whose behavior the forecasts must then capture. It is the joints between these layers, they find, where the field is weakest.

Forecasting emerges from the analysis as the foundational layer of the entire stack. Machine learning models that predict solar generation, wind output, electricity prices, and consumption profiles shape every downstream decision: a community that misjudges tomorrow’s solar peak will schedule its battery charging badly, trade at the wrong times, and potentially violate grid constraints. The review shows that optimization and energy storage control increasingly rely on learning-based and hybrid AI frameworks—combinations of reinforcement learning, deep learning, and classical mathematical programming—to manage uncertainty and juggle competing objectives such as cost, emissions, battery longevity, and fairness among members. Hybrid approaches are particularly attractive because pure learning methods can be brittle and hard to certify, while pure optimization struggles when the underlying models of the world are wrong.

Yet the authors’ cross-cutting synthesis reveals a sobering pattern: tight coupling between forecasting, optimization, and physical execution remains rare. Most studies build one layer in isolation and assume the others behave ideally. A forecasting paper rarely tests whether its predictions actually improve a battery schedule; an optimization paper rarely checks whether its assumptions match the forecast errors of a real prediction model. This fragmentation has direct consequences for robustness, scalability, and fairness—qualities that matter enormously when an algorithm is deciding whose heat pump gets curtailed during a price spike or whose electric vehicle charges first.

The review’s maturity assessment makes the deployment gap concrete. The authors coded each of the 130 studies on four practical dimensions: constraint realism, baseline comparison, reproducibility support, and validation setting. The good news is that most studies now model explicit technical, operational, market, or grid-related constraints, suggesting the literature has moved beyond purely abstract algorithmic demonstrations. Baseline comparison is common, though uneven—a substantial share of papers provides only limited comparative evidence, making it hard to judge whether claimed performance gains are real or artifacts of weak benchmarks.

Reproducibility, however, is the field’s most glaring weakness. Only a very small subset of studies provides clear access to code, open datasets, or implementation details sufficient for independent replication. In a domain where results can hinge on subtle data preprocessing, hyperparameter choices, or simulation assumptions, this lack of transparency undermines benchmarking and slows collective progress. It also makes it harder for utilities, cooperatives, and regulators to distinguish genuinely deployable methods from impressive-looking simulations.

Validation practice tells a similar story. The overwhelming majority of the reviewed studies were evaluated purely in simulation. Some used real-world datasets, and only a smaller number progressed to pilot projects, living labs, or field-oriented testing. The transition from methodological development to deployment-oriented evidence—demonstrating that an AI pipeline survives contact with noisy sensors, communication delays, hardware failures, and real human behavior—remains limited. This matters because energy communities are socio-technical systems: their success depends not just on algorithms but on the people who join them, the markets they participate in, and the grids they connect to.

The integration assessment goes deeper still. The authors coded each study on whether forecasts actually flowed into decisions, whether the system operated in closed loop, how storage execution was modeled, how uncertainty was represented—deterministic, probabilistic, scenario-based, or robust-set-based—whether downstream impacts were evaluated, and whether strategic interactions among agents were captured. The coding was deliberately conservative, classifying features only when explicitly documented, and the full study-level matrix is published in the paper’s appendices so that other researchers can inspect and reuse the analysis. This kind of transparent, mapping-oriented synthesis is itself a methodological contribution, giving the field its first unified picture of where intelligence actually connects to action.

From these findings, the review charts a research agenda built around three ideas: decision-centric forecasting, in which prediction models are designed and trained with their downstream use in mind rather than for standalone accuracy; asset-aware control, in which storage management respects the physical and lifecycle realities of batteries rather than treating them as idealized buffers; and market-compatible AI, in which community-level algorithms can interact credibly with electricity markets and grid operators. The authors argue that progress will require the field to stop optimizing components in isolation and start building, testing, and reporting integrated pipelines—ideally with open code and field validation.

For a world racing to electrify heating, transport, and industry while flooding the grid with variable renewables, the stakes of this research agenda are high. Energy communities promise cheaper, cleaner, more resilient power and a more active role for consumers, but only if the software that orchestrates them is robust, fair, and trustworthy. This review provides both a candid diagnosis of the current state of the art and a roadmap for closing the gap between clever algorithms and communities that actually run on them. The message for researchers, funders, and startups alike is clear: the next breakthrough in AI-driven energy systems will come not from a better forecaster or a smarter optimizer, but from making the pieces work together.

Subject of Research: Artificial intelligence methods for forecasting, optimization, and energy storage management in decentralized energy communities

Article Title: Artificial intelligence for forecasting, optimization, and energy storage in energy communities: a review

Article References: Lezama, F., Dias de Lima, T., Castro, F., Bairrao, D., & Vale, Z. (2026). Artificial intelligence for forecasting, optimization, and energy storage in energy communities: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11681-9

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11681-9

Keywords: artificial intelligence, energy communities, forecasting, optimization, energy storage systems, machine learning, reinforcement learning, smart grids, renewable energy, systematic review, energy management, decentralized energy

The authors begin from a diagnosis of fragmentation. Previous surveys have typically zeroed in on a single application domain, such as short-term forecasting of solar and wind output, smart grid control, or materials discovery for carbon capture, and each of those silos has produced genuinely useful insights. The problem, they argue, is that the clean energy transition is a system-of-systems challenge, in which a forecasting model feeds a trading algorithm, which shapes storage dispatch, which determines whether a hydrogen electrolyzer runs on surplus renewables or on fossil-heavy electricity. By treating these domains as separate literatures, researchers have missed the coupling effects that determine real-world sustainability outcomes. The new review stitches those threads together, assessing applications across renewable energy forecasting, energy storage, hydrogen and bioenergy, carbon capture, utilization, and storage, and recycling pathways within the circular economy.

Technically, the review highlights how data-driven models have matured across each of these domains. In renewable forecasting, machine learning systems now blend satellite imagery, numerical weather prediction, and sensor telemetry to predict generation at horizons ranging from minutes to days, reducing the uncertainty that grid operators must cover with reserves. In storage, algorithms learn charge and discharge strategies that extend battery life while arbitraging price volatility. In hydrogen and bioenergy, models screen catalysts, optimize fermentation and gasification conditions, and predict yield from variable feedstocks. In carbon capture and utilization, learning systems accelerate the search for sorbents and solvents and tune capture processes that were historically too energy-intensive to scale. In recycling and circular economy flows, computer vision and robotics sort waste streams with a speed and accuracy that manual sorting cannot match, while predictive models estimate the recoverable value of end-of-life products before they are disassembled.

One of the review’s most distinctive contributions is its insistence that technical performance alone is an incomplete measure of success. The authors devote substantial attention to how artificial intelligence supports life cycle assessment and techno-economic analysis, the two workhorse methods used to judge whether a clean technology actually reduces environmental burdens and whether it can survive in the market. Life cycle assessment traces the emissions, resource use, and impacts of a product from raw material extraction through disposal, while techno-economic analysis models costs, revenues, and financial risk. Machine learning can automate the data collection and uncertainty quantification that make these analyses slow and expensive, and it can link laboratory-scale innovations to system-level metrics of sustainability and cost-effectiveness. In the authors’ framing, an algorithm that improves a catalyst by a few percent is only interesting if the life cycle and economic models confirm that the improvement survives scaling.

The review is equally candid about the obstacles that keep these tools out of deployment. Data scarcity tops the list: industrial facilities generate sparse, proprietary, and inconsistently labeled records, and many clean technology processes, from novel electrolyzers to pilot capture plants, simply lack the training data that modern deep learning assumes. Model interpretability follows closely. Operators and regulators are reluctant to hand control of critical infrastructure to neural networks whose reasoning they cannot inspect, a concern the authors connect to the growing field of explainable AI. Algorithmic bias can skew models toward the conditions and geographies represented in their training data, quietly disadvantaging regions with different climates, grids, or industrial bases. Cybersecurity risks compound the problem, since increasingly networked energy systems present attack surfaces that legacy supervisory control architectures were never designed to defend, a challenge the co-author team, which includes researchers from a network and data security laboratory in Sichuan, examines explicitly.

Integration with legacy infrastructure emerges as another stubborn barrier. Power grids, refineries, and waste plants were engineered around deterministic control loops and decades-old communication protocols, and retrofitting them with learning-based systems raises questions of compatibility, reliability, and liability that no amount of algorithmic elegance can resolve on its own. The review’s cross-sectoral perspective makes this point sharper: a forecasting model may be accurate enough for a modern control room, yet worthless if the substation it informs runs on equipment that cannot accept probabilistic inputs. The authors argue that deployment pathways must therefore be engineered alongside the algorithms themselves, with transition architectures that let intelligent and conventional controls coexist while infrastructure catches up.

Perhaps the most uncomfortable section of the review turns the lens on artificial intelligence itself. Training large-scale models is energy-intensive, and data center electricity demand is rising rapidly as AI adoption accelerates across the economy. The authors argue that promoting AI as an enabler of sustainability while ignoring its own footprint would be a category error, and they call for sustainable digital practices: efficiency-aware model design, responsible siting of compute, and honest accounting of the emissions attributable to AI systems. This self-critical stance distinguishes the review from much of the enthusiasm-driven literature and aligns it with a growing scholarly debate over whether the computational costs of machine learning can be justified by the operational savings it delivers in energy systems.

To move from diagnosis to action, the authors propose a roadmap resting on four pillars. First, open datasets, so that results can be reproduced and benchmarked across laboratories and industries rather than locked inside proprietary silos. Second, physics-informed and interpretable AI models that embed physical laws and engineering constraints directly into the learning process, improving accuracy in data-poor regimes and giving engineers and regulators a transparent basis for trust. Third, ethical and governance frameworks that address accountability, fairness, and safety before, not after, systems are deployed at scale. Fourth, stronger alignment between technology, policy, and markets, so that algorithms trained to optimize technical objectives are not fighting against regulatory structures and price signals that point in a different direction. The roadmap is explicitly aimed at three audiences at once: researchers who need shared data and evaluation standards, industry leaders who need integration pathways, and policymakers who need decision support grounded in evidence.

The timing of the review gives that policy dimension particular weight. With national commitments to net-zero emissions hardening into legislation, and with AI capabilities advancing faster than the governance structures meant to steer them, the window for deliberate, coordinated deployment is narrowing. The authors’ unifying message is that artificial intelligence should be positioned not as a silver bullet but as an enabler of an equitable, resilient, and sustainable clean energy future, one whose benefits depend on responsible engineering choices made now. By consolidating more than 450 studies into a single cross-sectoral synthesis, and by pairing its catalog of technical advances with a sober accounting of data, interpretability, bias, security, and footprint challenges, the review offers researchers, industry, and governments something the fragmented literature could not: a shared map of where AI in clean technologies stands, and a concrete account of what it will take to get where it needs to go.

Subject of Research: Cross-sectoral applications of artificial intelligence in clean energy technologies and sustainability

Article Title: Artificial intelligence for clean technologies: a cross-sectoral review of sustainable, data-driven, and policy-aware systems

Article References: Ejiyi, C. J., Wei, L., Eze, T. F., Nnani, A. O., Gu, Y. H., Al-antari, M. A., Bamisile, O. O., & Cai, D. (2026). Artificial intelligence for clean technologies: a cross-sectoral review of sustainable, data-driven, and policy-aware systems. Journal of Big Data. https://doi.org/10.1186/s40537-026-01555-w

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01555-w

Keywords: artificial intelligence, clean technologies, renewable energy forecasting, carbon capture, life cycle assessment, techno-economic analysis, explainable AI, circular economy, smart grids, energy storage, hydrogen, AI governance

The research focuses on one of the most demanding charging environments in Türkiye: the Bolu-Elmalık region, the busiest charging location on the Anatolian Highway that connects Istanbul and Ankara, one of the country’s most heavily trafficked intercity corridors. Seventeen charging stations in the region were analyzed, and crucially, the study did not rely on simulated or synthetic data. Real-time consumption measurements were obtained directly from the local distribution system operator, Sakarya Elektrik Dağıtım A.Ş., known as SEDAS. This grounding in operational grid data gives the findings a credibility that laboratory-scale experiments often lack, because the irregular, spiky, and highly variable consumption patterns of highway charging stations are exactly the kind of signal that defeats naive forecasting approaches.

The context for this work is a market in the midst of a genuine boom. While Türkiye’s electric vehicle market initially lagged behind Europe’s, recent years have seen a dramatic acceleration, driven by regulations issued by the Electricity Market Regulatory Authority, a rapid increase in the number of charging operators and installed charging stations, and the arrival of electric vehicles priced comparably to conventional cars. The entry of domestically produced electric vehicles into the market has further accelerated adoption. As the authors note, this surge in vehicle numbers translates directly into rapidly rising energy demand, which in turn necessitates new investments in power grids and the development of flexible, accessible grid infrastructure. Forecasting is the foundation of that planning process.

At the heart of the study lies a two-level hybrid stacking ensemble model, an architecture that combines the strengths of several different learning algorithms rather than betting on any single one. In the first stage, three primary learners make independent predictions of hourly consumption: the Extra Trees Regressor, the LightGBM Regressor, and the XGBoost Regressor. All three belong to the family of tree-based ensemble methods, which build large collections of decision trees and aggregate their outputs, but they differ in how those trees are constructed and how aggressively they correct their own errors. Extra Trees introduces additional randomness in tree splitting to reduce variance, while LightGBM and XGBoost are gradient boosting methods that build trees sequentially, with each new tree trained to fix the residual mistakes of its predecessors.

The clever part of the stacking design is what happens next. Instead of simply averaging the three base learners’ outputs, the model feeds their predictions into a second-stage meta-learner built on Ridge Regression, a regularized form of linear regression. Ridge Regression, originally introduced by Hoerl and Kennard in 1970, adds a penalty term that shrinks coefficients and guards against overfitting, which is particularly valuable when the inputs to the meta-learner, the outputs of correlated tree models, are themselves highly interrelated. By letting a simple, stable linear model learn the optimal weighting of the three powerful but heterogeneous tree models, the stacking framework captures complex nonlinear patterns in the first stage while maintaining accuracy and stability in the final prediction. This division of labor is precisely what allowed the hybrid model to significantly outperform every individual base learner on its own.

Before any modeling began, the researchers confronted a problem that plagues many machine learning applications in energy systems: multicollinearity among input variables. When predictors are strongly correlated with one another, models can become unstable and their interpretations misleading. The team assessed this using Variance Inflation Factor analysis, a standard statistical diagnostic that quantifies how much the variance of an estimated coefficient is inflated by correlation among the predictors, allowing problematic variables to be identified and handled prior to model training. This preprocessing step reflects a broader lesson for applied machine learning: careful statistical hygiene before training often matters as much as the sophistication of the algorithm itself.

Interpretability received equally serious attention. The researchers applied SHAP analysis, short for SHapley Additive exPlanations, a technique rooted in cooperative game theory that assigns each input feature a contribution value for every individual prediction. Developed by Lundberg and Lee, SHAP has become the gold standard for explaining the behavior of complex ensemble models, and here it served two purposes: identifying the most influential attributes driving the consumption forecasts and pinpointing which of the seventeen stations exerted the greatest influence on model estimates. For grid operators, this kind of transparency is not a luxury. Understanding why a model predicts a demand surge, and which stations or temporal patterns drive it, transforms a black-box forecast into an actionable planning tool.

The performance numbers tell a compelling story. In addition to the R² of 0.979, the hybrid model achieved a mean absolute error of 69.906 kilowatt-hours and a root mean square error of 100.745 kilowatt-hours in hourly consumption forecasting. In practical terms, this means the model can track the hourly load profile of a busy highway charging hub with errors small enough to be genuinely useful for operational decisions. The authors emphasize that the approach aims to achieve high accuracy and stability in time-series consumption forecasting by combining the powerful learning capabilities of tree-based heterogeneous models under a regularized linear meta-model, a formulation that balances flexibility with robustness in a way single models struggle to match.

The implications extend well beyond one highway corridor in Türkiye. Accurate forecasting of load profiles and consumption patterns at electric vehicle charging stations enables improvements across a range of critical grid functions, including optimal grid planning, demand-side management, grid flexibility, load shifting, and peak shaving. Peak shaving, in particular, is a pressing concern: uncoordinated fast charging can create sharp demand spikes that force utilities to invest in expensive peaking capacity or risk overloading local transformers. A reliable hourly forecast allows operators to anticipate those spikes, shift flexible loads, deploy storage strategically, and defer costly infrastructure upgrades. As electric vehicle adoption accelerates globally, the gap between charging demand and grid capacity will widen in many regions, and tools like this stacking ensemble offer a way to manage that transition intelligently rather than reactively.

The study was carried out within the GARDEN project, short for Grid-Aware Decarbonization of Electricity-driven Neighbourhoods, and was supported by the Scientific and Technological Research Council of Türkiye under the 1071 Programme within the Driving Urban Transitions Partnership, co-funded by the European Commission. The authors gratefully acknowledge SEDAS for providing the charging data that made the analysis possible. While data privacy considerations mean the underlying dataset cannot be shared, the methodology itself, combining multicollinearity screening, heterogeneous tree-based base learners, a regularized meta-learner, and explainability analysis, offers a replicable blueprint for distribution system operators anywhere facing the same challenge. As the electric vehicle revolution rolls onward, the grids that power it will increasingly depend on models like this one to see the demand coming before it arrives.

Subject of Research: Hybrid machine learning forecasting of regional electric vehicle charging demand from real distribution grid data

Article Title: Regional EV charging demand estimation based on real grid data using a hybrid stacking ensemble model

Article References: Regional EV charging demand estimation based on real grid data using a hybrid stacking ensemble model. (n.d.). https://doi.org/10.1007/s10586-026-06533-8

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06533-8

Keywords: EV charging demand forecasting, machine learning, stacking ensemble, XGBoost, LightGBM, Extra Trees, Ridge Regression, SHAP, power grid planning, electric vehicles, time-series forecasting, smart grids