Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Many market models implement this principle to predict electricity prices but typically require certain assumptions and simplifications. In this article, we present an explainable machine learning model for the prices on the German day-ahead market, which substantially outperforms a benchmark model based on the merit order principle. Our model is designed for the ex-post analysis of prices and thus builds on various external features. Using Shapley Additive exPlanation (SHAP) values, we can disentangle the role of the different features and quantify their importance from empiric data. Load, wind and solar generation are most important, as expected, but wind power appears to affect prices stronger than solar power does. Fuel prices also rank highly and show nontrivial dependencies, including strong interactions with other features revealed by a SHAP interaction analysis. Large generation ramps are correlated with high prices, again with strong feature interactions, due to the limited flexibility of nuclear and lignite plants. Our results further contribute to model development by providing quantitative insights directly from data.

Artificial Intelligence (AI) is no longer a flashy term thrown around without merit. Rather, industries across the board are broadening use of technology applications to improve industry standards and drive greater efficiency. Over centuries of operating supply chains, humans improved operations to the point where shipments could be completed in less than 24 hours. But with technology entering the picture only a few decades ago, and quickly advancing to the point of surpassing human intelligence, it's time to investigate specific areas across the industry where technology can further enhance operations, simplify and automate tasks, boost safety measures and, ultimately, improve bottom lines. AI enables machines to mimic human processes, all while doing it better.

Artificial Intelligence is rolling out across the aviation industry to a greater and greater extent. It could even hold the key to a speedier post-pandemic recovery. Let's take a look at how its branch of machine learning is already impacting everyday aspects of travel, including how tickets are priced, point-to-point routes, fuel consumption optimization, and biometric boarding. "AI is coming and it will have no mercy for any obstacles on its way. Companies can choose to resist and maintain status quo to extend their survival period, or embrace AI and be part of the ongoing revolution," – IATA, AI in Aviation White Paper, 2018.

Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. Accurate forecasting of IDP migration would empower humanitarian aid groups to more effectively allocate resources during conflicts. We show that monthly flow of IDPs from province to province in both Syria and Yemen can be accurately forecasted one month in advance, using publicly available data. We model monthly IDP flow using data on food price, fuel price, wage, geospatial, and news data. We find that machine learning approaches can more accurately forecast migration trends than baseline persistence models. Our findings thus potentially enable proactive aid allocation for IDPs in anticipation of forecasted arrivals.