采用人工神经网络作为非线性函数逼近器，对两组建筑能耗参数和太阳辐射数据进行逼近。在建模（培训）阶段，要预测的数据不可用，这为该技术提供了一个“盲”测试。第一个时间序列包括1989年9月至12月的建筑能源“输入”（如太阳辐射和温度），需要预测1990年1月至2月的能源使用情况。仅利用手头的数据进行推断。 尽管冷冻水和热水使用的结果是可以接受的，但电力使用的预测将从容易获得的额外信息中显著受益，例如工作日和非工作日。第二个时间序列需要从四次全球方向测量中预测光束太阳辐射。这是一个插值问题，对这个数据集进行了很好的预测。采用共轭梯度和级联相关神经网络程序。关键词: 人工智能、专家系统、建筑、热负荷、能源消耗、计算机程序、精度、太阳辐射、温度、计算、竞赛:研讨会，ASHRAE Trans。1994年，第100卷，第2部分

Artificial neural nets were used as nonlinear function approximators on two data sets of building energy parameters and solar radiation data. During the modelling (training) phase, the data to be predicted were unavailable, providing a "blind" test of the technique. The first time series consisted of building energy "inputs" (such as solar radiation and temperature) for September-December 1989 and required the prediction of energy use for January-February 1990. The extrapolation was performed with only the data immediately at hand. Although results for chilled- and hot-water use were acceptable, the prediction of electricity use would have benefited markedly from easily available additional information, such as working and nonworking days. The second time series required the prediction of beam solar insolation from four global directional measurements. This was an interpolation problem, and good predictions were achieved for this data set. Conjugate gradient and cascade correlation neural net programs were used.KEYWORDS: artificial intelligence, expert systems, buildings, heat load, energy consumption, computer programs, accuracy, solar radiation, temperature, calculating, competition