Forecasts inform decision-making in nearly every domain. Forecasts are often produced by experts with rare or hard to acquire skills. In practice, forecasts are often used by domain experts and managers with little forecasting expertise. Our study focuses on how to design forecasting software that empowers non-expert users. We study how users can make use of state-of-the-art forecasting methods, embed their domain knowledge, and how they build understanding and trust towards generated forecasts. To do so, we co-designed a forecasting software prototype using feedback from users and then analyzed their interactions with our prototype. Our results identified three main considerations for non-expert users: (1) a safe stepwise approach facilitating causal understanding and trust; (2) a white box model supporting human-reasoning-friendly components; (3) the inclusion of domain knowledge. This paper contributes insights into how non-expert users interact with forecasting software and by recommending ways to design more accessible forecasting software.

SP Energy Networks is investing in cutting-edge software that applies machine learning algorithms and data science to predict electricity network demand and generation output. The artificial intelligence (AI) forecasting software, which uses historical network data and detailed weather data to make the predictions, will enable the network operator to maximise capacity and reliability across the electricity distribution network. Sia's software will go live in March 2020 and will be used in the real-time management of the network and forward planning when assessing the impact of new connections across the system. The investment comes as Britain's electricity network experiences a rapid transition from fossil fuel generation to renewable energy, low carbon options and energy efficiency programmes. Grant McBeath, Control Room Manager at SP Energy Networks, said: "Demand on the network is forecast to increase considering all future energy scenarios as we transition towards a zero carbon economy. We, therefore, have to change the way we manage the network – transitioning from passive approach to much more active and agile management, which requires a more dynamic approach to ensure capacity is maximised and customers' supplies remain uninterrupted. "Working with Sia on forecasting software will allow us a better understanding of the future flows of energy on the network right down to a half hourly basis.