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

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.

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

“…More specifically, the choice of a temporal data granularity (data sampling frequency) for specifying consumption load profile features has a crucial impact on the results of any action or assessment, as discussed in the literature [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], see Table 1 . This table summarizes for each potential action or assessment the time resolution (data granularity) and time horizon (time slice) envisaged for the works related to load profiles in households.…”

“…Hoevenaars [ 64 ] showed that using a 1-h time step hid the load variability within the hour for models of renewable power systems. Regarding optimization purposes, Van der Meer [ 42 ] concluded that a 5-min time resolution provided a good balance between accuracy and the burden of data size, whereas [ 45 ] showed that using hourly data led to large biases compared to 1-min data. However, coarser data could be sufficient for household aggregation [ 48 ].…”

“…Furthermore, even though the current trend is to use more temporally granular data sets in household applications, the influence of temporal granularity has not as yet been analyzed using a comprehensive and high-resolution data set. Quite a few papers [ 38 , 39 , 40 , 41 , 42 , 45 , 48 , 65 ] evaluated the bias due to the use of coarse-grained data, but never compared resolutions lower than 1 to 5 min. Still another shortcoming is the fact that most studies cover short time slices.…”

Probabilistic reference model for hourly PV power generation forecasting

“…A nonparametric approach to obtain the density forecast is presented in [10]. The effect of the aggregation of time series of electricity load and the increasing share of PV power in the net load is evaluated in [11] by means of prediction intervals (PIs) in local electricity distribution grids.…”

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

“…In spite of the length of the time period, from the perspective of the data structure, the input of the prediction model is a time series of the power load. In fact, besides the previous situation, the power load is also related to many factors such as seasons, meteorological conditions and people's living habits [3,4]. In order to improve the forecasting accuracy of load, the influence of these factors must be considered.…”