In the electricity grid, constantly balancing the supply and demand is critical for the network’s stability and any expected deviations require balancing efforts. This balancing becomes more challenging in future energy systems characterised by a high proportion of renewable generation due to the increased volatility of these renewables. In order to know when any balancing efforts are required, it is essential to predict the so-called net load, the difference between forecast energy demand and renewable supply. Although various forecasting approaches exist for both the individual components of the net load and the net load itself, it is unclear if it is more beneficial to aggregate several specialised forecasts to obtain the net load or to aggregate the input data to forecast the net load with one approach directly. Therefore, the present paper compares three net load forecasting approaches that exploit different levels of aggregation. We compare an aggregated strategy that directly forecasts the net load, a partially aggregated strategy that forecasts demand and supply separately, and a disaggregated strategy that forecasts demand and supply from each generator separately. We evaluate the forecast performance of all strategies with a simple and a complex forecasting model, both for deterministic and probabilistic forecasts, using one year of data from a simulated realistic future energy system characterised by a high share of renewable energy sources. We find that the partially aggregated strategy performs best, suggesting that a balance between specifically tailored forecasting models and aggregation is advantageous.
With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in nonuniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments.
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