A multilayered‐type neural network is attractive for daily electric load forecasting because the neural network can acquire a nonlinear relationship among the electric load data and their factors (weather, temperature, etc.) automatically. This paper discusses first some essential issues to be considered in neural network applications. One is difficulty of obtaining sufficient effective training data, another is the influence of abnormal learning data, and one more is the inevitable outerpolation. For these issues, the following three methods are developed in order to forecast more accurately: (1) a structure of the neural networks for insufficient training data; (2) detection and diminishing the influence of abnormal data; (3) employment of interpolation network and outerpolation network with additional data for outerpolation. Furthermore, to increase the sensitivity between electric loads and factors, (4) removal of base load is developed. Those methods work effectively to decrease the average absolute errors of peak‐load forecasting and 24‐hour load forecasting to 1.78 percent and 2.73 percent, respectively.