Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

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

Assessing the impact of employing machine learning-based baseline load prediction pipelines with sliding-window training scheme on offered flexibility estimation for different building categories

The present study is focused on assessing the impact of the performance of baseline load prediction pipelines on the estimation (by the grid operator) accuracy of the flexibility offered by different categories of buildings. Accordingly, the corresponding impact of employing different machine learning (ML) algorithms, with sliding-window and offline training schemes, for hour-ahead baseline load prediction has been investigated and compared. Using a smart meter measurements dataset, training window sizes and the most promising pipeline for each building category are first identified. Next, the consumption profiles of five buildings (belonging to each category), with the regular operation (baseline load) and while offering flexibility, are physically simulated. Finally, the identified pipelines are used for predicting the baseline loads, and the resulting error in estimating the provided flexibility is determined. Obtained results demonstrate that the identified most promising prediction pipeline (extra trees algorithm with a sliding window of 5 weeks) offers a notably superior performance compared to that of offline training (average score of 0.91 vs. 0.87). Employing these pipelines permits estimating the provided flexibility with acceptable accuracy (flexibility index's mean relative error between -2.45% to +2.79%), permitting the grid operator to guarantee fair compensation for buildings' offered flexibility.publishedVersio

Laminar convective heat transfer of shear-thinning liquids in rectangular channels with longitudinal vortex generators

Heat and fluid flow in a rectangular channel heat sink equipped with longitudinal vortex generators have been numerically investigated in the range of Reynolds numbers between 25 and 200. Aqueous solutions of carboxymethyl cellulose (CMC) with different concentrations (200–2000 ppm), which are shear-thinning non-Newtonian liquids, have been utilised as working fluid. Three-dimensional simulations have been performed on a plain channel and a channel with five pairs of vortex generators. The channels have a hydraulic diameter of 8 mm and are heated by constant wall temperature. The vortex generators have been mounted at different angles of attack and locations inside the channel. The shear-thinning liquid flow in rectangular channels with longitudinal vortex generators are described and the mechanisms of heat transfer enhancement are discussed. The results demonstrate a heat transfer enhancement of 39–188% using CMC aqueous solutions in rectangular channels with LVGs with respect to a Newtonian liquid flow (i.e. water). Additionally, it is shown that equipping rectangular channels with LVGs results in an enhancement of 24–135% in heat transfer performance vis-à-vis plain channel. However, this heat transfer enhancement is associated with larger pressure losses. For the range of parameters studied in this paper, increasing the CMC concentration, the angle of attack of vortex generators and their lateral distances leads to an increase in heat transfer performance. Additionally, heat transfer performance of rectangular channels with longitudinal vortex generators enhances with increasing the Reynolds number in the laminar flow regime.(OLD) MSE-