An asymmetrical and quadratic Support Vector Regression loss function for Beirut short term load forecast

Load forecasting is a critical necessity in the electricity industry since any unanticipated demand could cause possible grid instability and blackouts. Ideally, the capacity should be kept slightly above the current demand to avoid any undesired outages and suboptimal last minute power purchase. Motivated to develop an intelligent and efficient forecasting approach, we propose investigating in this paper the impact of using a loss function in Support Vector Regression (SVR) that is modified with a strict mandate to minimize under estimating power needs. Experimental results for the municipality of Beirut's power substations show that the number of under-predictions was drastically reduced from an average of 50% to 1.91% with a very minimal impact of 0.3% on average on the error rate which motivates follow on research.