Developing an “Information Process Map” of Improved Forecasts for Solar Energy
In this talk we present results of efforts to characterize the process of information creation, transmission, and use for decision making and the subsequent economic value of improved decisions. This process involves multiple stakeholders with different objectives, resources, and constraints as well as different levels of technical sophistication. The process begins with weather observations, modeling and analysis, and forecasting. This information is transmitted to a range of intermediaries and end-users including commercial vendors, energy traders, and utility and transmission grid operators. Ultimately the economically benefit of this information is measured in terms of improved decision making and outcomes from the generation of solar energy. Understanding these economic benefits should be a critical driver for focusing efforts to improve forecasts.
The objectives of this socio-economic analysis are (1) to develop initial estimate of the value of improved forecasts and (2) assess approaches to measuring forecast improvements (e.g., metrics) that are meaningful to both the weather forecasters and the solar energy and utility end-users. We thus plan to explicitly tie benefit measures to metrics of forecast quality (e.g., “user-relevant verification”).
First, through a process of focus groups, individual interviews, and iterated analysis we develop a graphical model that we are labeling an “Information Process Map.” Drawing on methods from concept mapping, mental modeling, influence diagraming, and cognitive task analysis, we develop this map to understand the process of value creation. This map serves to better articulate the different stakeholders needs and capacity at different points in the process as well as to identify potential gaps along the process.
Second, we conducted a small set of in-depth interviews using expert elicitation to further evaluate the information process map and derive initial estimates of the value of improved forecasts. Expert elicitation allows us to develop initial benefit estimates without undertaking a full economic analysis and can help identify key issues in value creation and information use. The process map and expert interviews also serve to develop and ground-truth a broader survey effort. With respect to this survey effort, we discuss development of a discrete choice experiment (DCE) to elicit preferences for improved forecasts using metrics developed as Task 1 of the broader solar energy-weather forecast improvement research project.