Adaptive optimal monthly peak building demand limiting strategy based on exploration-exploitation tradeoff

Peak demand limiting is an efficient means to reduce the monthly electricity cost in cases where peak demand charge is a major factor. This paper presents an adaptive optimal monthly peak building demand limiting strategy based on exploration and exploitation tradeoff in threshold resetting. Two basis function components are developed, including a building load prediction model and an optimal threshold resetting scheme. The building load prediction model is built using the artificial neural network (ANN). The optimal threshold resetting scheme is developed based on the cost-benefit analysis, and the predicted building demands and/or actual building power uses. Three basic exploration-exploitation tradeoff schemes (i.e., the non-greedy, the greedy and the ε-greedy schemes) are proposed for optimal threshold resetting. Monte Carlo simulation is conducted to analyze the impacts of the exploration-exploitation tradeoff scheme parameter on the demand limiting performance under uncertainties. The model validation results show that the ANN building load prediction model can achieve satisfactory accuracy with the average mean absolute percentage error (MAPE) of 5.7%. Case studies are conducted and the results show that the strategy based on the three proposed schemes can effectively reduce the monthly peak demand cost in different seasons. Monte Carlo simulation results show that the ε-greedy scheme could achieve higher monthly net cost saving with better robustness when a large value of ε is used in both winter and summer.