Abstract
Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.
Highlights
Buildings consume nearly 50% of the electricity [1], and account for almost 40% of the greenhouse gas emissions
demand response (DR) refers to “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” [4], which is mainly applied through two categories in power systems, incentive-based programs (IBPs) and price-based programs (PBPs) [5]
Based on the tree model, the model based control with a random forest (MBCRF) learning algorithm is developed in this paper for demand response that the comprehensive energy consumption model of the building is learned among various features
Summary
Buildings consume nearly 50% of the electricity [1], and account for almost 40% of the greenhouse gas emissions. Based on the Monte Carlo method and dynamic pricing, authors in [12] developed a robust demand response control of commercial buildings for a smart grid under load prediction uncertainty. In [18], a model based control with a regression trees method was exploited for optimal DR strategies for large commercial buildings. Zhang et al [19] studied the learning mechanism with an optimization method for DR application, in which the neural network-based learning and regression-based learning were used to obtain the HVAC energy consumption model, respectively. Inspired by the aforementioned facts, this paper proposes a novel model based control with a random forest (MBCRF) DR learning algorithm for an office building. A novel model based control with a random forest (MBCRF) learning algorithm is developed for the optimal DR control strategies.
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