Grid-interactive efficient buildings (GEBs) can provide flexibility services to the grid through demand response. This paper presents a novel predictive modeling methodology to estimate the availability of electrical demand flexibility in GEBs under demand response schemes. In this context, a physics-based energy simulation model of a reference building, considering the cooling demand in the summer season as the flexible load, is utilized. Accordingly, the impact of increasing the indoor setpoint temperature by 1.5 °C (for a maximum of 3 hours per day), which enables the demand side flexibility with a reduction of the cooling equipment’s electrical load, is simulated. Next, each demand response event is gathered, sorted, and then used to train the model to predict similar future events over the same time horizon in the following days. For this purpose, a deep neural network model trained using an expanding window training scheme is utilized to predict (15 minutes before the event) the load in the next 3 hours while undergoing the flexibility scenario. It is demonstrated that, with four months of training data, the model offers a promising prediction accuracy with a Mean Absolute Percentage Error (MAPE) of 3.55%.
This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted.
The charging load of electric vehicles, the magnitude of which is expected to increase, creates complex balancing challenges for the power grid. Elevated thermal inertia of warehouses offers a promising flexibility potential that can be leveraged as a buffer in case of high power demands to avoid blackouts or notable increments in the user's cost of energy owing to the rise in the peak load. The present work investigates the feasibility of utilizing a conditioned warehouse's flexibility by modulating the indoor air temperature's setpoint to reduce the demand while electric trucks are being charged. Within this framework, energy simulation of a cooled fine storage warehouse has been used while considering the scenario of 2 electric trucks being charged (for a night shift delivery) immediately after the offices' are closed. The possibility of providing sufficient power to partially charge the trucks without exceeding the building's peak demand by increasing the warehouse's setpoint temperatures by 2.5 °C (for a maximum of 4 hours each day) has been investigated. It was found that the proposed approach enables the charging of the two electric trucks on 60% of the days of the cooling season (for an average duration of 170 minutes).
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