In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

Energy policy makers need information about the greenhouse gas reduction potential that could be realized by changes to the operation of the currently existing European power plant fleet to enable short-term actions. Possible measures could reduce the climate impact of the European electricity system and, additionally, be realized quickly as new investments are avoided. In this paper, the Calliope based energy system model Stella of the European electricity system is presented and used for the first time, with the goal to quantify cost and CO 2 emissions optimal operation strategies of the existing European power plant fleet. By applying the model to six scenarios the results show that the greenhouse gas emissions of the European power plant fleet could be reduced by more than 50% with little additional costs compared to today’s power generation mix. It is shown that historic power plant operation follows only economic considerations while not fully covering its climate impact. The results demonstrate to policy makers the scale of reduction potential that could be achieved by short-term actions.