TY - RPRT TI - Short-term forecasts of district heating load and outdoor temperature by use of on-line connected computers; Korttidsprognoser foer fjaerrvaermelast och utetemperatur med on-linekopplade datorer AB - In this report the available methods for forecasting weather and district heating load have been studied. A forecast method based on neural networks has been tested against the more common statistical methods. The accuracy of the weather forecasts from the SMHI (Swedish Meteorological and Hydrological Institute) has been estimated. In connection with these tests, the possibilities of improving the forecasts by using on-line connected computers has been analysed. The most important results from the study are: Energy company staff generally look upon the forecasting of district heating load as a problem of such a magnitude that computer support is needed. At the companies where computer calculated forecasts are in use, their accuracy is regarded as quite satisfactory; The interest in computer produced load forecasts among energy company staff is increasing; At present, a sufficient number of commercial suppliers of weather forecasts as well as load forecasts is available to fulfill the needs of energy companies; Forecasts based on neural networks did not attain any precision improvement in comparison to more traditional statistical methods. There may though be other types of neural networks, not tested in this study, that are possibly capable of improving the forecast precision; Forecasts of outdoor temperature and district heating load can be significantly improved through the use of on-line-connected computers supplied with instantaneous measurements of temperature and load. This study shows that a general reduction of the load prediction errors by approximately 15% is attainable. For short time horizons (less than 5 hours), more extensive load prediction error reductions can be reached. For the 1-hour time horizon, the possible reduction amounts to up to 50%. 21 refs, 4 figs, 7 appendices AU - "Malmstroem, B" AU - "Ernfors, P" AU - "Nilsson, Daniel" AU - "Vallgren, H [Chalmers Tekniska Hoegskola, Goeteborg (Sweden). Institutionen foer Energiteknik]" KW - "29 ENERGY PLANNING AND POLICY" KW - "32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION" KW - "DISTRICT HEATING" KW - "LOAD MANAGEMENT" KW - "ENERGY DEMAND" KW - "FORECASTING" KW - "WEATHER" KW - "METEOROLOGY" KW - "AMBIENT TEMPERATURE" KW - "ON-LINE SYSTEMS" KW - "NEURAL NETWORKS" KW - "MATHEMATICAL MODELS" KW - "NUMERICAL DATA" DO - https://doi.org/ UR - https://www.osti.gov/etdeweb/servlets/purl/414558 PB - CY - Sweden PY - 1996 DA - 1996-10-01 LA - Swedish J2 - [] VL - C1 - Stiftelsen foer Vaermeteknisk Forskning, Stockholm (Sweden) C2 - C3 - SVF-589 C4 - C5 - OSTI as DE97722060 L3 - Other Information: DN: Figures and tables with text in English; PBD: Oct 1996 ER -