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