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DDC: 620 × Type: Text × Publisher: Amsterdam [u.a.] : Elsevier Science × WGL Institute: IHP ×

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    High-resolution net load forecasting for micro-neighbourhoods with high penetration of renewable energy sources
    (Amsterdam [u.a.] : Elsevier Science, 2020) Kobylinski, P.; Wierzbowski, M.; Piotrowski, K.
    Though extensive, the literature on electrical load forecasting lacks reports on studies focused on existing residential micro-neighbourhoods comprising small numbers of single-family houses equipped with solar panels. This paper provides a full description of an ANN-based model designed to predict short-term high-resolution (15-min intervals) micro-scale residential net load profiles. Since it seems especially relevant due to the specificity of local autocorrelations in load signal, in this paper we put stress on the systematic approach to feature selection in the context of lagged signal. We performed a case study of a real micro-neighbourhood comprising only 75 single-family houses. The obtained average prediction error was equivalent to 5.4 per cent of the maximal measured net load. The issues, i.e.: (1) the feasibility of micro-scale residential load forecasting taking into account renewable energy penetration, (2) the feasibility to predict net load with dense temporal resolution of 15 min, (3) the feature selection problem, (4) the proposed prosumption- and comparison-oriented prediction model key performance measure, could be of interest to engineers designing energy balancing systems for local smart grids. © 2019 The Authors
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    Prediction of solar particle events with SRAM-based soft error rate monitor and supervised machine learning
    (Amsterdam [u.a.] : Elsevier Science, 2020) Chen, J.; Lange, T.; Andjelkovic, M.; Simevski, A.; Krstic, M.
    This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in space applications by combining the real-time Soft Error Rate (SER) measurement with SRAM-based detector and the offline trained machine learning model. The proposed approach is intended for the self-adaptive fault-tolerant multiprocessing systems employed in space applications. With respect to the state-of-the-art, our solution allows for predicting the SER 1 h in advance and fine-grained hourly tracking of SER variations during SPEs as well as under normal conditions. Therefore, the target system can activate the appropriate mechanisms for radiation hardening before the onset of high radiation levels. Based on the comparison of five different machine learning algorithms trained with the public space flux database, the preliminary results indicate that the best prediction accuracy is achieved with the recurrent neural network (RNN) with long short-term memory (LSTM). © 2020 The Authors