Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals

By Dennis van der Meer, Joakim Munkhammar, Joakim Widén. Solar Energy, 2018

75 citations recorded by the source corpus.

Abstract This paper presents a study into the effect of aggregation of customers and an increasing share of photovoltaic (PV) power in the net load on prediction intervals (PIs) of probabilistic forecasting methods applied to distribution grid customers during winter and spring. These seasons are shown to represent challenging cases due to the increased variability of electricity consumption during winter and the increased variability in PV power production during spring. We employ a dynamic Gaussian process (GP) and quantile regression (QR) to produce probabilistic forecasts on data from 300 de-identified customers in the metropolitan area of Sydney, Australia. In case of the dynamic GP, we also optimize the training window width and show that it produces sharp and reliable PIs with a training set of up to 3 weeks. In case of aggregation, the results indicate that the aggregation of a modest number of PV systems improves both the sharpness and the reliability of PIs due to the smoothing effect, and that this positive effect propagates into the net load forecasts, especially for low levels of aggregation. Finally, we show that increasing the share of PV power in the net load actually increases the sharpness and reliability of PIs for aggregations of 30 and 210 customers, most likely due to the added benefit of the smoothing effect.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a6191dd62228830a3c95a28de266a5d598753a95

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In Artificial Intelligence

In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, methodology): “In recent literature, several methods are used to generate PIs [35] and optimise [36] further in order to enhance their efficiency against uncertainties due to seasonal or geographical variations [37].”