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 R2 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

PRICISE TARGET GEOLOCATION BASED ON INTEGERATION OF THERMAL VIDEO IMAGERY AND RTK GPS IN UAVS

There are an increasingly large number of uses for Unmanned Aerial Vehicles (UAVs) from surveillance, mapping and target geolocation. However, most of commercial UAVs are equipped with low-cost navigation sensors such as C/A code GPS and a low-cost IMU on board, allowing a positioning accuracy of 5 to 10 meters. This low accuracy which implicates that it cannot be used in applications that require high precision data on cm-level. This paper presents a precise process for geolocation of ground targets based on thermal video imagery acquired by small UAV equipped with RTK GPS. The geolocation data is filtered using a linear Kalman filter, which provides a smoothed estimate of target location and target velocity. The accurate geo-locating of targets during image acquisition is conducted via traditional photogrammetric bundle adjustment equations using accurate exterior parameters achieved by on board IMU and RTK GPS sensors and Kalman filtering and interior orientation parameters of thermal camera from pre-flight laboratory calibration process

3D SURFACE GENERATION FROM AERIAL THERMAL IMAGERY

Aerial thermal imagery has been recently applied to quantitative analysis of several scenes. For the mapping purpose based on aerial thermal imagery, high accuracy photogrammetric process is necessary. However, due to low geometric resolution and low contrast of thermal imaging sensors, there are some challenges in precise 3D measurement of objects. In this paper the potential of thermal video in 3D surface generation is evaluated. In the pre-processing step, thermal camera is geometrically calibrated using a calibration grid based on emissivity differences between the background and the targets. Then, Digital Surface Model (DSM) generation from thermal video imagery is performed in four steps. Initially, frames are extracted from video, then tie points are generated by Scale-Invariant Feature Transform (SIFT) algorithm. Bundle adjustment is then applied and the camera position and orientation parameters are determined. Finally, multi-resolution dense image matching algorithm is used to create 3D point cloud of the scene. Potential of the proposed method is evaluated based on thermal imaging cover an industrial area. The thermal camera has 640×480 Uncooled Focal Plane Array (UFPA) sensor, equipped with a 25 mm lens which mounted in the Unmanned Aerial Vehicle (UAV). The obtained results show the comparable accuracy of 3D model generated based on thermal images with respect to DSM generated from visible images, however thermal based DSM is somehow smoother with lower level of texture. Comparing the generated DSM with the 9 measured GCPs in the area shows the Root Mean Square Error (RMSE) value is smaller than 5 decimetres in both X and Y directions and 1.6 meters for the Z direction

Modulating the HVAC demand of a wharehouse to provide load flexibility for charging electric trucks

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)

Handling complete short-term data logging failure in smart buildings: Machine learning based forecasting pipelines with sliding-window training scheme

This paper implements a machine learning(ML)-based procedure for constructing the missing sensor(s) data in a net zero energy building in case of complete failure in data recording (for up to one hour). In the first scenario, missing temperature data is re-created using the sensor's ex-ante data, the HVAC system's status flag, and the ambient conditions. In the second scenario, the temperature data (until failure occurred) from two close-by spaces are also utilized as inputs. For each scenario, ML-based pipelines' performance is first assessed by considering different prediction horizons using a benchmark algorithm. Next, each pipeline's most promising features and the most suitable algorithm are identified. Using the obtained optimal pipeline, a sliding window-based training scheme is implemented, and the size of the training window is optimized. It is shown that feature selection, algorithm optimization procedures, and the sliding window-based training scheme notably improve the forecasting performance. The proposed methodology can be deployed as a tool in intervals with total data logging failure, providing data to ML-based controllers in smart buildings and avoiding disruptions in the building management system