摘要：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.
其他摘要：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.
