Abstract
In smart grid era, electric load is becoming more stochastic and less predictable in short horizons with more intermittent energy and competitive electricity market transactions. As a result, short-term probabilistic load forecasting (STPLF) is becoming essential for energy utilities because it helps quantify the risks of decision-making for power systems operation. Currently, probabilistic load forecasts (PLF) are commonly produced from three single components, namely input, model and output. Nevertheless, whether integrating two components to represent dual uncertainties of electric load is practical and able to improve STPLF attracts little regards. To address this issue, this paper proposes three integrated methods by pairwise combination of single representative component, i.e. uniform-biased temperature scenarios (UBTS), quantile regression (QR) and logarithmic residual empirical simulation (LRES). Case study on real utility data demonstrates the superiority of the integrated methods and excavates the relationship between predictive model class and specific integrated method.
Highlights
Grid modernization with more intermittent energy and competitive electricity market transactions has exacerbated the volatility and uncertainty of electric load, making it less predictable even in short horizons than ever before
Researchers and forecasters mainly produced probabilistic load forecasts from three single components of forecasting engines [5], i.e. (1) the input component: simulating predictors to generate input scenarios; (2) the model component: constructing probabilistic forecasting models or multiple predictive models; (3) the output component: output post-processing via residual simulation or forecasting combination
In Gradient Boosting Regression Tree (GBRT) model class, logarithmic residual empirical simulation (LRES)-uniform-biased temperature scenarios (UBTS) generates the best probabilistic load forecasts on average and outperforms the better method most, which is similar to quantile regression (QR)-LRES in Multi-linear regression (MLR) case
Summary
Grid modernization with more intermittent energy and competitive electricity market transactions has exacerbated the volatility and uncertainty of electric load, making it less predictable even in short horizons than ever before. Probabilistic load forecasting, offering intervals, quantiles and probability density as prediction formats, has become significant and urgent for utilities to help make risk analysis and practical decision in power systems operation and planning [1]. In the input component, Xie and Hong [6] adopted fixed-date, shifted-date and bootstrap methods to generate temperature scenarios for probabilistic load forecasting; in the model component, Yang et al [7] proposed Gaussian process quantile regression, a dual probabilistic model, to produce probability density for electric load; in the output component, Fan and Hyndman [8] generated day-ahead prediction intervals for electric load in Australian National Electricity Market by simulating forecasting residuals via block bootstrapping
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