Refine
Year of publication
- 2020 (4) (remove)
Document Type
- Master's Thesis (4) (remove)
Language
- English (4) (remove)
Has Fulltext
- yes (4)
Keywords
- Corporate Development (1)
- Deep Transformation Model (1)
- Digitally re-programmable space (1)
- Internet of Things (1)
- Knowledge Management (1)
- Machine Learning (1)
- Normalizing Flow (1)
- Platform (1)
- Regression (1)
- Smart Building (1)
- Smart City (1)
- Wissensmanagement (1)
Institute
Forecasting is crucial for both system planning and operations in the energy sector. With increasing penetration of renewable energy sources, increasing fluctuations in the power generation need to be taken into account. Probabilistic load forecasting is a young, but emerging research topic focusing on the prediction of future uncertainties. However, the majority of publications so far focus on techniques like quantile regression, ensemble, or scenario-based methods, which generate discrete quantiles or sets of possible load curves. The conditioned probability distribution remains unknown and can only be estimated when the output is post-processed using a statistical method like kernel density estimation.
Instead, the proposed probabilistic deep learning model uses a cascade of transformation functions, known as normalizing flow, to model the conditioned density function from a smart meter dataset containing electricity demand information for over 4,000 buildings in Ireland. Since the whole probability density function is tractable, the parameters of the model can be obtained by minimizing the negative loglikelihood through the state of the art gradient descent. This leads to the model with the best representation of the data distribution.
Two different deep learning models have been compared, a simple three-layer fully connected neural network and a more advanced convolutional neural network for sequential data processing inspired by the WaveNet architecture. These models have been used to parametrize three different probabilistic models, a simple normal distribution, a Gaussian mixture model, and the normalizing flow model. The prediction horizon is set to one day with a resolution of 30 minutes, hence the models predict 48 conditioned probability distributions.
The normalizing flow model outperforms the two other variants for both architectures and proves its ability to capture the complex structures and dependencies causing the variations in the data. Understanding the stochastic nature of the task in such detail makes the methodology applicable for other use cases apart from forecasting. It is shown how it can be used to detect anomalies in the power grid or generate synthetic scenarios for grid planning.
Throughout this thesis, the implementation of tools for knowledge management as a key factor for sustainable corporate development, is presented. In industries with a high fluc-tuation rate, such as construction, efficient knowledge management is of particular im-portance. Companies feel the effects of negligent handling of this resource especially dur-ing the Corona pandemic. Restructuring leads to experienced employees leaving the com-pany – and with them the know-how and experience gained. With a systematic knowledge transfer, the most important insights in such situations remain within the or-ganization. Thus, the company becomes crisis-proof and receives all the tools it needs to grow healthily again after the recession. Practical data from competitors indicates that knowledge management promises savings potential of several million euros per year for BAM. Further potentials in the areas of sustainability, customer- and employee satisfac-tion as well as occupational safety, which do not lead to savings, are also worth mention-ing. This thesis determines the current maturity level of knowledge management at BAM, before introducing processes and systems that successively drive the improvement. The developed methods simultaneously help to prevent and solve problems and systematical-ly promote the continuous improvement of all work processes in the company.
Detailed steps are presented to carry out change management towards the successful introduction and further development of knowledge management at BAM. A major focus is on interpersonal factors. The related topic of knowledge culture was recently ranked by german think-tank Zukunftsinstitut as one of the top 5 megatrends for companies in the 2020s. The methods developed, contribute to the creation of such a culture and to the transformation of BAM towards a learning organization. Knowledge management identi-fies with the BAM values. In the course of this thesis it will be shown how the system by its very nature, helps to implement these values in the work of every employee.
The results of this elaboration were recently awarded the Digital Construction Award 2020 for Business Excellence at BAM Deutschland AG.
Botswana, a new construction project – the Maun Science Park - is to be built with a focus on sustainability and to create a new living space for the rapidly growing population in Africa. The project will be a blueprint for future projects in Africain terms of progress, technology and sustainability. This thesis will deal with its financial framework and will serve as a basis for the development of ways and means of financing such projects.
Cities around the world are facing an increasing number of global and local challenges, such as climate change and scarcity of raw materials. At the same time trends like digitalization, globalization and networking gain in importance. For this reason, cities have started imple-menting smart solutions within the urban structure in order to evolve towards a Smart City. In Botswana, the Maun Science Park is intended to provide a best practice approach for a Bot-swanan Smart City. Since Smart City concepts have to be specifically tailored to local condi-tions, the first main goal of this thesis is to develop a synthesis concept for the Maun Science Park. A key problem in cities is the utilization of space, which is further intensified by increasing urbanization and population growth. Therefore, the second main goal is to develop approaches of (digitally) re-programmable space to use available areas intelligently and optimized.
Within the thesis, human-centered design has been applied as structure-giving methodology. By clarifying relevant Smart City contents, considering reference examples as well as identify-ing local challenges and requirements, an appropriate concept has been developed with hu-man-focus. Furthermore, the methodologies of literature research and expert interviews have been used as input in the individual human-centered design phases. In combination with an innovation funnel, the methodology human-centered design forms the structure of the thesis.
In total, ten main solution areas and 37 sub-segments have been identified for the synthesis concept of Maun Science Park. Additionally, a concept for Smart Buildings has been devel-oped as a part of the synthesis concept and as an essential infrastructure component of the Maun Science Park (three main segments, 16 sub-segments). Based on expert input, a priori-tization has been determined by evaluating the impact and economic affordability of the indi-vidual sub-areas. Moreover, individual key areas have been highlighted by identifying direct interactions between sub-segments and on the basis of expert input – these are particularly related to the segments Smart Data and Smart People. Besides the synthesis concept, ap-proaches of (digitally) re-programmable space have been created. Thereby, ten approaches refer to the conversion, reuse or expansion abilities of space within daily, weekly or life cycle. In addition, the conventional (digitally) re-programmable space idea has been extended by two new considerations – “multi-purpose use of built-up space” and “concept programming in the planning phase”. Finally, within an overall consideration – synthesis concept combined with approaches of (digitally) re-programmable space – the added value of the developed contents has been outlined, positive and negative aspects have been identified within a SWOT analysis and the business model of the Maun Science Park approach has been verified in a Business Model Canvas.
Through explicit elaboration, classification and prioritization of solution areas, the developed concept can serve as a basis for further project steps. Based on the defined requirements of the sub-segments, solutions can be developed with regard to the entire Smart City context.