Short-term load forecasting for educational buildings with temperature correlation

To obtain a precise short-term load forecast for an education institute, the correlation between its electric demands and temperatures needs to be analyzed because electric demand is sensitive to temperature changes. With the aim of developing an improved short-term load forecasting algorithm for an arbitrary education institute, fluctuations in the daily, weekly, and yearly load patterns of a university campus in Seoul were analyzed and correlated with temperature trends during the respective periods. The data were then compared with past load patterns. An optimal exponential smoothing coefficient according to the selected period was used for the building load forecasts. The estimated optimal exponential smoothing coefficient derived for each selected period was then compared with past load patterns. The proposed algorithm was verified by simulation of the electric demands of the campus in 2011. The test results show that the forecasting accuracy of the proposed algorithm is improved comparing with traditional exponential smoothing analysis.