57 research outputs found
The impact of imperfect weather forecasts on wind power forecasting performance: Evidence from two wind farms in Greece
Weather variables are an important driver of power generation from renewable energy sources. However, accurately predicting such variables is a challenging task, which has a significant impact on the accuracy of the power generation forecasts. In this study, we explore the impact of imperfect weather forecasts on two classes of forecasting methods (statistical and machine learning) for the case of wind power generation. We perform a stress test analysis to measure the robustness of different methods on the imperfect weather input, focusing on both the point forecasts and the 95% prediction intervals. The results indicate that different methods should be considered according to the uncertainty characterizing the weather forecasts
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic
operation of power grids and energy markets. However, the non-linearity and
non-stationarity of electricity demand as well as its dependency on various
external factors renders STLF a challenging task. To that end, several deep
learning models have been proposed in the literature for STLF, reporting
promising results. In order to evaluate the accuracy of said models in
day-ahead forecasting settings, in this paper we focus on the national net
aggregated STLF of Portugal and conduct a comparative study considering a set
of indicative, well-established deep autoregressive models, namely multi-layer
perceptrons (MLP), long short-term memory networks (LSTM), neural basis
expansion coefficient analysis (N-BEATS), temporal convolutional networks
(TCN), and temporal fusion transformers (TFT). Moreover, we identify factors
that significantly affect the demand and investigate their impact on the
accuracy of each model. Our results suggest that N-BEATS consistently
outperforms the rest of the examined models. MLP follows, providing further
evidence towards the use of feed-forward networks over relatively more
sophisticated architectures. Finally, certain calendar and weather features
like the hour of the day and the temperature are identified as key accuracy
drivers, providing insights regarding the forecasting approach that should be
used per case.Comment: Keywords: Short-Term Load Forecasting, Deep Learning, Ensemble,
N-BEATS, Temporal Convolution, Forecasting Accurac
Improving the forecasting performance of temporal hierarchies
Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias
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