Figure 1: Machine Learning (ML), at its most simple form, is just pattern recognition. Based on historical data, it is possible to use different models to explain various behaviors. While ML is being trained, it is learning; when it has finished learning, it’s considered a model. When the model is implemented, it will no longer learn, but it will recognize known patterns. This could include forecasting maintenance, supply temperature, pressure, or any other data-related aspect. Humans engage in pattern recognition all the time; however, the complexity of these patterns limits human capabilities. ML can process almost an infinite amount of complexity in patterns.
SMART-Meter model The first AI initiative, named “SMART-Meter,” aimed to create an AI model for forecasting heat load on substations 12-24 hours in advance. Leveraging AI’s core capability in pattern recognition, the primary goal was to prove the feasibility of predicting heat load for a single substation. Subsequent goals included expanding this capability to predict heat load for specific substation types (e.g., kindergartens, offices, residential/ business mixes) and different heat supply types (e.g., room heating only vs. room heating and hot water). Success was defined by achieving the first hypothesis, but if the latter two were also possible, it would significantly reduce development costs and facilitate the training of generalized AI models. For the initial PoC, two substations designed for approximately 1MW, both primarily serving households (over 80% customer base) and representing standard network configurations, were chosen: one with a specific heating type (Room heating) and another with a mixed heating type (Room heating and hot water). The first substation’s 12-hour forecast showed an average deviation of ±7.14kW, and its 48-hour forecast deviated by ±8.28kW. For the second substation, the 12-hour forecast had an average deviation of ±6.21 kW, and its 48-hour forecast deviated by ±6.60 kW. While the models effectively captured heat load trends for typical customers and heating types, highly fluctuating patterns presented a challenge. The results suggest the feasibility of developing “type” substation models using the same underlying code, which could substantially lower implementation and maintenance costs, making a much more substantial return on investment. Hydraulic Weak Point Model The second initiative, “Hydraulic Weak Point,” aimed to train a model that would provide data-driven feedback to operators.
Less is more, forget big data! “Throughout the project, BEW demonstrated strong capabilities in data availability and quality, which was instrumental in enabling a successful Proof of Concept (PoC) in just two months.” - Maria Hvid, CAIO, Neurospace Figure 2: Any AI project mandatorily requires the inclusion of domain experts, as their specialized knowledge is crucial for guiding the AI development process, interpreting results accurately, and ensuring the models are relevant and effective within the specific problem domain. This collaboration ensures that AI is not just technologically advanced but also deeply integrated with the practical realities and nuances of the field, leading to more successful and impactful outcomes.
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