In this paper, fuzzy (inaccuracy, vague, dispersion of individual interpretation) and random (incompleteness, noise and variability) uncertainties of electric load forecasting are modeled by random fuzzy variables (RFVs). Further integrating it into neural networks (NN) to formulate a novel integrated technique-Random Fuzzy NN (RFNN) for load forecasting is presented. The features of this methodology are as follows. (1) It is able to effectively and simultaneously model referred uncertainties occurred in load forecasting by one integrated technique, which existing techniques (e.g. fuzzified NN or Bayesian NN) tackle them separately. (2) Specially, historical data/information containing incompleteness, inaccuracy and vagueness can be modeled by the proposed representations of RFVs. No preprocessing algorithms such as data imputation or discard are required. (3) The proposed RFNN can make NN incorporating both types of uncertainties of inputs and network parameters so as to possess with better tackling uncertainties of load forecasting than other relevant methods. The proposed techniques are applied to electric load forecasting using a real operational data collected from Macau electric utility. Its application is promising in microgrid/small power system or in forecasting curves of individual customer where load curves would present a much higher variability and more noise than global curves of power grid of one country/region. © 2015 Elsevier Ltd.