Semantic-Aware Cross-Domain Multi-Horizon Energy Forecasting for Renewable-Aware Manufacturing: The NEO-FORECAST Framework

In the modern world, factories are beginning to run on a different kind of clock. For decades, industrial power was a steady, predictable river, flowing from the grid to the machines with little variation. Today, that river is becoming a mix of steady flow and sudden, weather-dependent surges from solar panels and wind turbines. This shift creates a complex puzzle for factory managers. They must decide when to run heavy machinery, when to store energy in batteries, and when to buy power from the grid, all while trying to keep costs low and production steady. To make these decisions, they need forecasts that are not just accurate, but also aware of the specific physical reality of their situation. A prediction for a sunny day's solar output is fundamentally different from a prediction for a factory's machine usage, even if both are measured in kilowatts. Treating these different types of energy data as if they were the same kind of thing can lead to confusion and poor decisions.

This is the challenge that Adnan Haider Zaidi addresses in a new study called NEO-FORECAST. The research focuses on a specific problem in energy forecasting: how to use what we know about one type of energy system to help predict another, without making mistakes. Imagine trying to learn how to drive a truck by first learning how to ride a bicycle. The skills of balancing and steering are related, but the vehicles are so different that blindly applying bicycle rules to a truck could be dangerous. In energy forecasting, this is known as "negative transfer," where mixing data from different sources actually makes the predictions worse rather than better. The study asks a precise question: under what conditions does sharing knowledge between different energy tasks help, and when does it hurt? The researchers developed a framework to test this, ensuring that they do not simply assume that more data is always better, but instead carefully check if the data is compatible before using it.

The core of the NEO-FORECAST framework is a method that respects the unique identity of each energy source. Instead of dumping all available data—electricity demand, solar generation, building usage, and industrial loads—into a single blender, the system keeps them distinct. It recognizes that while these systems might share some patterns, like daily cycles or reactions to weather, their underlying goals and physical behaviors are different. The researchers built a system that separates these patterns into three layers: the general time-based rhythms that might be shared, the specific traits of the location or domain, and the unique details of the individual entity. By keeping these layers separate, the system can decide when to borrow knowledge from another source and when to rely solely on its own history. This approach includes a "compatibility check," a mechanism that acts like a gatekeeper. Before the system allows knowledge to flow from one task to another, it calculates whether the two tasks are similar enough to benefit from the exchange. If they are too different, the gate closes to prevent the confusion that leads to negative transfer.

The study was designed with a rigorous focus on avoiding common pitfalls in scientific testing. A major concern in this field is "data leakage," where a model accidentally sees future information during its training, making it look smarter than it really is. The researchers implemented strict controls to ensure that the data used for training never touched the data used for testing, and that the testing was done in a way that respected the flow of time. They tested their method against several established models, including long-standing statistical tools and modern artificial intelligence systems like neural networks. The results showed that the NEO-FORECAST approach produced more accurate predictions than these standard methods. When looking at the average error rates, the new framework reduced the difference between the predicted and actual values significantly. Specifically, the average error was 5.9 compared to 8.2 for a standard recurrent model, and the variance, or spread of errors, was 2.6 compared to 3.7. In a separate test using normalized numbers to compare different scales, the new method scored 0.089, beating a transformer model at 0.112 and a tree-based model at 0.145.

However, the researchers are careful not to claim that this is a universal solution that works perfectly in every situation. The study explicitly argues against the idea that simply pooling all data together is the best strategy. They propose that the benefit of sharing knowledge depends heavily on the time horizon being predicted, and the framework is designed to evaluate this relationship through specific experiments rather than assuming it as a current capability. A prediction for the next hour might benefit from a different set of shared rules than a prediction for the next week, but the study defines this as a hypothesis to be tested against target-only training and other controls. Furthermore, the research emphasizes that a good forecast is not just about being close to the actual number; it is about how that number helps a factory make a decision. The study introduces a "decision interface" that connects the forecast to real-world actions, such as turning machines on or off. This allows the researchers to measure not just the accuracy of the prediction, but the value of the decision it enables. They propose a test to see if a slightly less accurate forecast can lead to a better operational decision, depending on the constraints of the factory, but note that this requires reproducible evidence to confirm when such a divergence occurs.

The ultimate goal of this work is to create a reproducible path from raw data to engineering decisions. The author acknowledges that while the aggregate numbers look promising, the true test lies in the details of specific datasets and specific manufacturing scenarios. They have laid out a clear plan for future verification, requiring that any claim of superiority be backed by evidence from specific experiments, not just general averages. The study does not claim to have solved the problem of energy forecasting for all of manufacturing. Instead, it provides a disciplined, evidence-controlled method for figuring out when and how to share knowledge between different energy tasks. By separating the general from the specific and checking for compatibility before sharing, the NEO-FORECAST framework offers a way to build smarter, more reliable energy systems for the renewable-aware factories of the future. The findings suggest that the future of energy management lies not in bigger, more complex models that try to learn everything at once, but in smarter systems that know exactly what they can learn from each other and what they must learn on their own.