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Transformers have recently gained prominence in long time series forecasting by elevating accuracies in a variety of use cases. Regrettably, in the race for better predictive performance the overhead of model architectures has grown onerous, leading to models with computational demand infeasible for most practical applications. To bridge the gap between high method complexity and realistic computational resources, we introduce the Residual Cyclic Transformer, ReCycle. ReCycle utilizes primary cycle compression to address the computational complexity of the attention mechanism in long time series. By learning residuals from refined smoothing average techniques, ReCycle surpasses state-of-the-art accuracy in a variety of application use cases. The reliable and explainable fallback behavior ensured by simple, yet robust, smoothing average techniques additionally lowers the barrier for user acceptance. At the same time, our approach reduces the run time and energy consumption by more than an order of magnitude, making both training and inference feasible on low-performance, low-power and edge computing devices. Code is available at https://github.com/Helmholtz-AI-Energy/ReCycle, Comment: 9 pages, 3 figures, to be published at IEEE CAI 2024, Associated code available at https://github.com/Helmholtz-AI-Energy/ReCycle

Load-forecasting problems have already been widely addressed with different approaches, granularities and objectives. Recent studies focus not only on deep learning methods but also on forecasting loads on single building level. This study aims to research problems and possibilities arising by using different load forecasting techniques to manage loads. For that the behaviour of two neural networks, Long Short-Term Memory and Feed Forward Neural Network and two statistical methods, standardized load profiles and personalized standardized load profiles are analysed and assessed by using a sliding-window forecast approach. The results show that machine learning algorithms have the benefit of being able to adapt to new patterns, whereas the personalized standardized load profile performs similar to the tested deep learning algorithms on the metrics. As a case study for evaluating the support of load-forecasting for applications in Energy management systems, the integration of charging stations into an existing building is simulated by using load forecasts to schedule the charging procedures. It shows that such a system can lead to significantly lower load peaks, exceeding a defined grid limit, and to a lower number of overloads compared to uncontrolled charging., Comment: 19 Pages, 16 Figures, 1 Postscript figure

This paper provides a formalization of the energy disaggregation problem for particle swarm optimization and shows the successful application of particle swarm optimization for disaggregation in a multi-tenant commercial building. The developed mathmatical description of the disaggregation problem using a state changes matrix belongs to the group of non-event based methods for energy disaggregation. This work includes the development of an objective function in the power domain and the description of position and velocity of each particle in a high dimensional state space. For the particle swarm optimization, four adaptions have been applied to improve the results of disaggregation, increase the robustness of the optimizer regarding local optima and reduce the computational time. The adaptions are varying movement constants, shaking of particles, framing and an early stopping criterion. In this work we use two unlabelled power datasets with a granularity of 1 s. Therefore, the results are validated in the power domain in which good results regarding multiple error measures like root mean squared error or the percentage energy error can be shown., Comment: 10 pages, 13 figures, 3 tables

Linear programming is used as a standard tool for optimising unit commitment or power flows in energy supply systems. For heat supply systems, however, it faces a relevant limitation: For them, energy yield depends on the output temperature, thus both quantities would have to be optimised simultaneously and the resulting problem is quadratic. As a solution, we describe a method working with discrete temperature levels. This paper presents mathematical models of various technologies and displays their potential in a case study focused on integrated residential heat and electricity supply. It is shown that the technique yields reasonable results including the choice of operational temperatures.

Solar cells used in building integration of photovoltaic cells (BIPV) are commonly made from crystalline wafer cells. This contribution investigates the challenges and benefits of using bifacial solar cells in vertical installations. We show that those cells get up to 13% more irradiance compared to optimum tilted south facing monofacial modules in Germany. The role of the n-layer in thin amorphous bifacial single-junction cells intended to be used as bifacial cells in BIPV applications is investigated. In contrast to the superstrate cell design, a transparent n-layer and back contact play a key role to achieve high bifaciality. We therefore increased the transparency of the n-layer by adding CO2, increasing the PH3 flow in the deposition gas and tested different thicknesses. With those measures, we reached a bifaciality of 98% for short-circuit current density and 99% for open-circuit voltage.

Solar cells used in building integration of photovoltaic cells (BIPV) are commonly made from crystalline wafer cells. This contribution investigates the challenges and benefits of using bifacial solar cells in vertical installations. We show that those cells get up to 13% more irradiance compared to optimum tilted south facing monofacial modules in Germany. The role of the n-layer in thin amorphous bifacial single-junction cells intended to be used as bifacial cells in BIPV applications is investigated. In contrast to the superstrate cell design, a transparent n-layer and back contact play a key role to achieve high bifaciality. We therefore increased the transparency of the n-layer by adding CO2, increasing the PH3 flow in the deposition gas and tested different thicknesses. With those measures, we reached a bifaciality of 98% for short-circuit current density and 99% for open-circuit voltage.

Solar cells used in building integration of photovoltaic cells (BIPV) are commonly made from crystalline wafer cells. This contribution investigates the challenges and benefits of using bifacial solar cells in vertical installations. We show that those cells get up to 13% more irradiance compared to optimum tilted south facing monofacial modules in Germany. The role of the n-layer in thin amorphous bifacial single-junction cells intended to be used as bifacial cells in BIPV applications is investigated. In contrast to the superstrate cell design, a transparent n-layer and back contact play a key role to achieve high bifaciality. We therefore increased the transparency of the n-layer by adding CO2, increasing the PH3 flow in the deposition gas and tested different thicknesses. With those measures, we reached a bifaciality of 98% for short-circuit current density and 99% for open-circuit voltage.