Cognitive Systems for the Food–Water–Energy Nexus

Abstract Providing for the food, water, and energy needs of a growing world population is a grand challenge. The way we choose to address this challenge as a society will have far-reaching impacts, for instance, on public health, national security, and the global climate. The interactions between the food, water, and energy systems offer us an opportunity to improve efficiency, but can also make the system more complex. Cognitive systems can help mitigate this complexity and thus improve efficiency. What food, water, and energy have in common is that they are often not produced where they are consumed, they are costly to transport, and they are hard to store efficiently in large quantities. And this is where cognitive computing comes in: if you cannot store a resource you must have good forecasts of supply and demand. This requires handling large scale datasets from multiple sources, using machine learning methods to build forecasting models, and leveraging optimization techniques to help incorporate forecasting results into a decision-making process. We will use energy as an example throughout the bulk of this chapter with the understanding that the same methods, challenges, and solutions can be applied more broadly to food, water, and other constrained resources. Sense: We will first discuss methods to make the most of sensor data. For example, it is expensive to deploy large networks of ground sensors (e.g., weather stations), and it is therefore beneficial to make use of sensors that can cover large areas, like radar or satellite images. However, it is challenging to estimate ground conditions based on satellite measurements alone. We will discuss machine learning algorithms that can learn the mapping from wide area sensor data to local conditions based on only a few ground-truth measurements. Applications include, for example, to estimate rainfall based on radar measurements, or to estimate solar power generation based on satellite images of clouds. Predict: Next, we will discuss forecasting methods ranging from a few minutes ahead to days or even years ahead. We will discuss forecasting methods for energy demand, solar energy generation, and wind generation. Different methods are effective for these different technologies as well as different forecasting horizons. Very short-range forecasts might use autoregressive models, while mid- to long-range forecasts may require physical models. We also discuss hybrid methods that combine expert knowledge with machine learning. React: Finally, we will discuss how to use the outputs of these analytics tools to serve decision-making processes. How optimization can help improve infrastructure planning and economic dispatch of power generation. And how cognitive systems can help system operators to make sense of messy data from multiple sources, provide recommended actions, and enable better decision making. We will highlight several different mathematical methods, including autoregressive models, generalized additive models, fully connected neural networks, deep learning, convolutional neural networks, and nonlinear optimization in the context of energy. We will conclude the chapter with an outlook on how current trends in the cognitive computing will impact the broader challenge of managing constrained resources.