Abstract
If one examines the spot price series of electrical power over the course of time, it is striking that the electricity price across the day takes a course that is determined by power consumption following a day and night rhythm. This daily course changes in its height and temporal extent in both, the course of the week, as well as with the course of the year. This study deals methodologically with non-linear correlative and autocorrelative time series properties of the electricity spot price. We contribute the usage of non-fully connectionist networks in relation to fully connectionist networks to decompose non-linear correlative time series properties. Additionally, we contribute the usage of long short-term-memory network (LSTM) to discover and to deal with autocorrelation effects.
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Meier, JH., Schneider, S., Schmidt, I., Schüller, P., Schönfeldt, T., Wanke, B. (2019). ANN-Based Electricity Price Forecasting Under Special Consideration of Time Series Properties. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_13
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