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Forecasting intermittent and lumpy demand is challenging. Demand occurs only sporadically and, when it does, it can vary considerably. Forecast errors are costly, resulting in obsolescent stock or unmet demand. Methods from statistics, machine learning and deep learning have been used to predict such demand patterns. Traditional accuracy metrics are often employed to evaluate the forecasts, however these come with major drawbacks such as not taking horizontal and vertical shifts over the forecasting horizon into account, or indeed stock-keeping or opportunity costs. This results in a disadvantageous selection of methods in the context of intermittent and lumpy demand forecasts. In our study, we compare methods from statistics, machine learning and deep learning by applying a novel metric called Stock-keeping-oriented Prediction Error Costs (SPEC), which overcomes the drawbacks associated with traditional metrics. Taking the SPEC metric into account, the Croston algorithm achieves the best result, just ahead of a Long Short-Term Memory Neural Network.
Digitalization increases the pressure for companies to innovate. While current research on digital transformation mostly focuses on technological and management aspects, less attention has been paid to organizational culture and its influence on digital innovations. The purpose of this paper is to identify the characteristics of organizational culture that foster digital innovations. Based on a systematic literature review on three scholarly databases, we initially found 778 articles that were then narrowed down to a total number of 23 relevant articles through a methodical approach. After analyzing these articles, we determine nine characteristics of organizational culture that foster digital innovations: corporate entrepreneurship, digital awareness and necessity of innovations, digital skills and resources, ecosystem orientation, employee participation, agility and organizational structures, error culture and risk-taking, internal knowledge sharing and collaboration, customer and market orientation as well as open-mindedness and willingness to learn.
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.
Demand forecasting intermittent time series is a challenging business problem. Companies have difficulties in forecasting this particular form of demand pattern. On the one hand, it is characterized by many non-demand periods and therefore classical statistical forecasting algorithms, such as ARIMA, only work to a limited extent. On the other hand, companies often cannot meet the requirements for good forecasting models, such as providing sufficient training data. The recent major advances of artificial intelligence in applications are largely based on transfer learning. In this paper, we investigate whether this method, originating from computer vision, can improve the forecasting quality of intermittent demand time series using deep learning models. Our empirical results show that, in total, transfer learning can reduce the mean square error by 65 percent. We also show that especially short (65 percent reduction) and medium long (91 percent reduction) time series benefit from this approach.
In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.
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.
The general conclusion of climate change studies is the necessity of eliminating net CO2 emissions in general and from the electric power systems in particular by 2050. The share of renewable energy is increasing worldwide, but due to the intermittent nature of wind and solar power, a lack of system flexibility is already hampering the further integration of renewable energy in some countries. In this study, we analyze if and how combinations of carbon pricing and power-to-gas (PtG) generation in the form of green power-to-hydrogen followed by methanation (which we refer to as PtG throughout) using captured CO2 emissions can provide transitions to deep decarbonization of energy systems. To this end, we focus on the economics of deep decarbonization of the European electricity system with the help of an energy system model. In different scenario analyses, we find that a CO2 price of 160 €/t (by 2050) is on its own not sufficient to decarbonize the electricity sector, but that a CO2 price path of 125 (by 2040) up to 160 €/t (by 2050), combined with PtG technologies, can lead to an economically feasible decarbonization of the European electricity system by 2050. These results are robust to higher than anticipated PtG costs.
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.