ovo je dobro danska ima taj sistem zato su lideri u tome u skandinaviji pametni narod SENN is also playing an important role in predicting the supply of wind energy. For instance, Siemens Wind Power, Denmark, has asked the SENN team to provide it with predictions on a 72-hour basis of the hourly output of a major wind park. In this context, SENN takes weather predictions, which are available only on a coarse grid pattern, and transforms them into local energy supply predictions. “With the rising stake of renewable energy sources such as wind in the total energy mix,” says Zimmermann, “utilities not only need to predict demand, but also supply. Prediction is important because it allows them to estimate when to activate back-up gas-fired generation.” With this in mind, Zimmermann’s team developed a neural network based on the major parameters that can affect wind power generation. “In such cases, the goal is to create a software model that is a mathematical representation of the real world,” says Zimmermann. But initially, he explains, the model does not know how important each parameter is — and that’s where learning from data comes into play (for more, see article “From Biological Systems to Machines, Learning is the key”). All the system knows at first is that, given the input it receives during its training phase, it will have to produce an output that is as close to the actual power output of the wind park as possible over time. At first, the discrepancy between model output and actual data is huge. But over time, the learning algorithm begins to modify the individual parameters within its model so that the predicted and actual results become closer and closer. By measuring its level of error over thousands of iterations, the system gradually moves from producing random outputs to identifying which combinations of weights on which input parameters result in which effects. “It’s like learning how to score a goal in a soccer game,” says Zimmermann. “All you know is that your output should be to get the ball into the net. Through a process of trial and error, and given the thousands of possible circumstances that can influence the result, you may learn to get it just right.” And SENN did get its prediction of the wind park’s output right. Its average error in terms of predicting the total energy supply of the park per day (calculated in terms of root mean square deviation) is now down to 7.2 percent — a full three percent better than the closest competing physics-based model. Similar models are currently being developed for photovoltaic plants. Quantifying the Unknown. Similarly, Zimmermann’s team has developed a neural network to model the nitrous oxide (NOX) emissions of gas turbines. Such a model can be used to analyze the relationships between numerous input variables and the output of a turbine over time. As with the case of the wind park, SENN began with only raw data and a mandate to describe actual output over time. Nevertheless, as it learned the relationships between variables the model grew closer and closer to duplicating the turbine’s behavior, and was eventually able to predict its behavior in real time with almost perfect accuracy. But of course there’s a lot more going on in a turbine — or any other complex system for that matter — than just its known variables. As Zimmermann points out, “There are variables that you cannot measure; and then there are those you do not even know about.” Such invisible variables can add up to a mountain of uncertainty. “In view of this,” says Zimmermann, “we have discovered a new way of explaining uncertainty — one that frames it as the interaction between observable and hidden variables.” By comparison, the standard approach to measuring uncertainty in mechanical and economic dynamic systems is to translate the deviation between what the model predicts and what actually happens in the real world into an estimate of risk. The underlying assumption is that the model of uncertainty measured in the past is a good estimator of future risk. “But this does not generally apply to predictions in the world of finance, which can include copper and electricity prices,” cautions Zimmermann. “Here, the idea is that uncertainty spreads from the present into the future as a diffusion process — scaled by measured historical model error — becoming larger and larger as we move forward through time.” In contrast, according to Zimmermann’s solution, since it is not possible to reconstruct hidden system variables unambiguously, you can quantify the amount of uncertainty in a prediction by analyzing the distribution of different scenarios that take shape. Here, the range of fluctuation between scenarios is interpreted as the level of risk, and a scenario based on the mean values from the different scenarios — all of which have the same probability — can be assumed to be the most probable future trend. “The resulting market risk is thus characterized by the variation between the scenarios,” says Zimmermann, who explains that, given a finite number of observations, there will always be multiple ways to reconstruct hidden variables, thus resulting in different scenarios for the future. Siemens already uses these methods to augment procurement decisions for energy and copper. “Instead of just a single model of the future,” adds Zimmermann, “this method provides a range of different future scenarios to be played out and evaluated.” How might the science of prediction evolve over the next few years? Clearly, if the past is any guide, we will see a steady progression toward increased accuracy. As Zimmermann points out, not only are SENN models learning more each day, but its creators are learning from the models it generates as they morph into closer and closer representations of reality. Massive Potential. Beyond forecasting energy and raw materials prices, beyond predicting the outputs of wind parks and turbines, SENN offers the potential for virtually limitless numbers of applications. It could help with some of the most challenging, complex and costly decisions of our time, namely those associated with urban and regional investment decisions in areas such as road, air traffic, water, and electrical infrastructures. Indeed, SENN’s potential as a decision support system is already being tested at Siemens to help determine, for instance, the relative long-term advantages of different sites before building a factory. And beyond that? A different model for our relationship with the future is taking shape in the form of a demonstration SENN Forecast Server now running on Siemens’ intranet. The system is being used to introduce internal customers to SENN’s potential. Fast forward ten years and we may be downloading SENN apps to monitor, learn from, diagnose, and optimize the functions of our homes, vehicles, businesses, and supply chains. SENN’s future versions may even be able to offer scenarios that support optimized, personalized nutritional, healthcare, educational, and financial paths. Every question, after all, has an answer that lies somewhere in the future. “The science of prediction,” says Zimmermann, “is a race between the increasing complexity of the real world and our accelerating ability to mathematically represent it by means of information-technology-related capabilities, such as SENN models.”
Posted on: Wed, 20 Aug 2014 10:58:54 +0000
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