FUZZY DATA-COMPRESSION FOR ENERGY OPTIMIZATION MODELS

Calculating energy savings and emission-reduction potentials for munic ipal energy systems requires computer models with high spatial and tem poral disaggregation. Consequently, the computational effort necessary to run such models is considerable. For the optimization models ecco, ecco-solar and deeco, this problem has so far been solved by assuming that the time intervals considered are independent of each other and may, therefore, be optimized consecutively. For minimization of primar y energy inputs and emission of pollutants, this approach is sufficien t. Optimization of monetary costs and placing of intertemporal emissio n limits, however, is not possible. Based on fuzzy-set theory, we deve lop a method with which the up to 8760 input-data sets of the model ca n be compressed by 1-2 orders of magnitude such that the remaining one s may then be optimized simultaneously. Test runs with original and co mpressed data sets show good agreement, thus proving the functioning o f the method. (C) 1997 Elsevier Science Ltd.