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Der spartenübergreifende BDI-Arbeitskreis Internet der Energie hat voraussichtliche Veränderungen durch Künstliche Intelligenz (KI) auf die Bereiche Energie und Klima analysiert und den möglichen Beitrag von KI zur Lösung anstehender Herausforderungen in diesen Bereichen erörtert. KI kann einen wesentlichen Beitrag zum Gelingen der Energiewende in Deutschland leisten. Der Energiesektor ist ein zentraler Bestandteil der deutschen Wirtschaft und daher auch in diesem Kontext äußerst relevant.
This paper addresses what we call the investment question: under what plausible circumstances, if any, can variable renewable energy (VRE, and solar photovoltaic (PV) in particular) be a good investment? Although VRE has been growing rapidly world-wide, it is generally subsidized. Under what cost and market conditions can solar PV flourish without subsidy? We employ solar insolation and market price data from the U.S. and from Germany to gain insight into the investment question. We find that unsubsidized solar PV is or may soon be a justifiable investment, but that market arrangements may play a crucial role in determining success. We end by sketching a proposal that amounts to a reformed capacity market that would afford participation of solar PV.
Machine learning (ML) techniques are rapidly evolving, both in academia and practice. However, enterprises show different maturity levels in successfully implementing ML techniques. Thus, we review the state of adoption of ML in enterprises. We find that ML technologies are being increasingly adopted in enterprises, but that small and medium-size enterprises (SME) are struggling with the introduction in comparison to larger enterprises. In order to identify enablers and success factors we conduct a qualitative empirical study with 18 companies in different industries. The results show that especially SME fail to apply ML technologies due to insufficient ML knowhow. However, partners and appropriate tools can compensate this lack of resources. We discuss approaches to bridge the gap for SME.
The promise of the EVs is twofold. First, rejuvenating a transport sector that still heavily depends on fossil fuels and second, integrating intermittent renewable energies into the power mix. However, it is still not clear how electricity networks will cope with the predicted increase in EVs and their charging demand, especially in combination with conventional energy demand. This paper proposes a methodology which allows to predict the impact of EV charging behavior on the electricity grid. Moreover, this model simulates the driving and charging behavior of heterogeneous EV drivers which differ in their mobility pattern, decision-making heuristics and charging strategies. The simulations show that uncoordinated charging results in charging load clustering. In contrast, decentralized coordination allows to fill the valleys of the conventional load curve and to integrate EVs without the need of a costly expansion of the electricity grid.
Mit der Energiewende hat die Bundesregierung den Umbau der Energieversorgung begonnen. Da das Gelingen der Energiewende für die Zukunfts- und Wettbewerbsfähigkeit des Wirtschaftsstandorts Deutschland essenziell ist, wurden seitens des Bundesverbandes der deutschen Industrie (BDI) bereits 2013 Impulse für eine smarte Energiewende veröffentlicht, in denen fünf Prinzipien abgeleitet werden, die einen Rahmen für den Diskurs über die zu ergreifenden Maßnahmen setzen. Erneuerbare Energien werden in dem kommenden Jahren die dominierende Stromquelle darstellen. Daraus entstehen neue Herausforderungen. Zu deren Bewältigung hat das Bundeswirtschaftsministerium (BMWi) kürzlich eine 10-Punkte-Agenda (ZPA) für die zentralen Vorhaben der Energiewende vorgelegt. Zu diskutieren ist, inwieweit sie im Einklang mit den fünf Prinzipien des BDI steht und an welchen Stellen Anpassungen notwendig werden, damit der Umbau des Energiesystems erfolgreich gelingen kann.
Zusammen mit Partnern aus Industrie und Politik untersuchen die ESB Business School der Hochschule Reutlingen, die Hochschule Offenburg und die Fachhochschule Nordwestschweiz (FHNW) in einem Interreg-Projekt die Möglichkeiten, klima- und gesundheitsschädliche Emissionen im Grenzverkehr am Hochrhein zu reduzieren. Elektromobilität und Fahrgemeinschaften werden dazu im Rahmen eines Pilotprojekts gefördert und die Wirkung analysiert. Erste Ergebnisse zeigen, dass heutige Elektroautos für das grenzüberschreitende Pendeln unter bestimmten Voraussetzungen geeignet sind.
The time has come : application of artificial intelligence in small- and medium-sized enterprises
(2022)
Artificial intelligence (AI) is not yet widely used in small- and medium-sized industrial enterprises (SME). The reasons for this are manifold and range from not understanding use cases, not enough trained employees, to too little data. This article presents a successful design-oriented case study at a medium-sized company, where the described reasons are present. In this study, future demand forecasts are generated based on historical demand data for products at a material number level using a gradient boosting machine (GBM). An improvement of 15% on the status quo (i.e. based on the root mean squared error) could be achieved with rather simple techniques. Hence, the motivation, the method, and the first results are presented. Concluding challenges, from which practical users should derive learning experiences and impulses for their own projects, are addressed.
Business process management and IT supported processes are an actual topic. The procedure of finding a business process system that implements your processes the best way is not easy and takes a lot of time. In this article you will find a recommendation for an open source system. Four selected open source workflow management systems are tested and analyzed. Mean criteria for the evaluation are listed in a criteria catalogue and rated by experts by their importance. Finally, the systems are evaluated by the criteria and the best evaluated system can be recommended.
In a networked world, companies depend on fast and smart decisions, especially when it comes to reacting to external change. With the wealth of data available today, smart decisions can increasingly be based on data analysis and be supported by IT systems that leverage AI. A global pandemic brings external change to an unprecedented level of unpredictability and severity of impact. Resilience therefore becomes an essential factor in most decisions when aiming at making and keeping them smart. In this chapter, we study the characteristics of resilient systems and test them with four use cases in a wide-ranging set of application areas. In all use cases, we highlight how AI can be used for data analysis to make smart decisions and contribute to the resilience of systems.
Since the beginning of the energy sector liberalization, the design of energy markets has become a prominent field of research. Markets nowadays facilitate efficient resource allocation in many fields of energy system operation, such as plant dispatch, control reserve provisioning, delimitation of related carbon emissions, grid congestion management, and, more recently, smart grid concepts and local energy trading. Therefore, good market designs play an important role in enabling the energy transition toward a more sustainable energy supply for all. In this chapter, we retrace how market engineering shaped the development of energy markets and how the research focus shifted from national wholesale markets to more decentralized and location-sensitive concepts.
