I currently have hard-coded in my forecasting model, 6 weightings (totaling 100%) for 6 weather stations and wish to determine a methodology to produce these weighting or conversion factors % to form an artificial single weather station. This is part of forecasting the electricity load in my city:- the data from 6 weather stations (observations of temperature (C), dew point (C) and rel. humidity (%)) is then weighted by the specific weightings and used further in the load's model equation. The request is for a conversion factor methodology that must capture the relevance of any of the 6 weather stations to the overall load. It must somehow combine temperature and dew point (since rel. humidity is equivalent here) within the weighting. To this end I have applied various regressions of electricity load against these station datasets (say temperature) without success and believe I need to scale or otherwise change my thinking. Sample data and current weightings in text file.