General regression neural networks (GRNN) were designed and trained to investigate the feasibility of using this technology to optimize HVAC thermal energy storage in public buildings as well as office buildings. State of the art building simulation software, ESP-r, was used to generate a database covering the years 1997-2001. The software was used to calculate hourly cooling loads for three office buildings using climate records in Kuwait. The cooling load data for 1997-2000 was used for training and testing the neural networks (NN), while robustness of the trained NN was tested by applying them to a "production" data set (2001 data) that the networks have never "seen" before. Three buildings of various densities of occupancy and orientational characteristics were investigated. Parametric studies were performed to determine optimum GRNN design parameters that best predict cooling load profiles for each building. External hourly temperature readings for a 24 h period were used as network inputs.. and the hourly cooling load for the next day is the output. The performance of the NN analysis was evaluated using a statistical indicator (the coefficient of multiple determination) and by statistical analysis of the error patterns, including confidence intervals of regression lines, as well as by examination of the error patterns. The results show that a property designed NN is a powerful instrument for optimizing thermal energy storage in buildings based only on external temperature records. (C) 2003 Elsevier Ltd. All rights reserved.