@article{ye_kim_hu_lu_wu_rehm_2023, title={A Modified Sequence-to-point HVAC Load Disaggregation Algorithm}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM52003.2023.10252553}, abstractNote={This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used as inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.}, journal={2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM}, author={Ye, Kai and Kim, Hyeonjin and Hu, Yi and Lu, Ning and Wu, Di and Rehm, P. J.}, year={2023} }
 @article{kim_ye_lee_hu_lu_wu_rehm_2023, title={An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data}, ISSN={["2167-9665"]}, DOI={10.1109/ISGT51731.2023.10066402}, abstractNote={This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods. Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.}, journal={2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT}, author={Kim, Hyeonjin and Ye, Kai and Lee, Han Pyo and Hu, Rongxing and Lu, Ning and Wu, Di and Rehm, P. J.}, year={2023} }
 @article{hu_ye_kim_lee_lu_wu_rehm_2023, title={Design Considerations of a Coordinative Demand Charge Mitigation Strategy}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM52003.2023.10252618}, abstractNote={This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable appliance loads, and conservation voltage reduction. All resources are directly controlled by load serving entities. A mixed integer linear programming-based energy management algorithm is developed to optimally coordinate DCM resources considering the load payback effect. To better capture system peak periods, two different kinds of load forecast are used: the day-ahead load forecast and the peak-hour probability forecast. Five DCM strategies are compared for reconciling the discrepancy between the two forecasting results. The DCM strategies are tested using actual utility data. Simulation results show that the proposed algorithm can effectively mitigate the demand charge while preventing the system peak from being shifted to the payback hours. We also identify the diminishing return effect, which can help load serving entities optimize the size of their DCM resources.}, journal={2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM}, author={Hu, Rongxing and Ye, Kai and Kim, Hyeonjin and Lee, Hanpyo and Lu, Ning and Wu, Di and Rehm, P. J.}, year={2023} }
 @article{kim_lee_2021, title={Probabilistic Solar Power Forecasting Based on Bivariate Conditional Solar Irradiation Distributions}, volume={12}, ISSN={["1949-3037"]}, DOI={10.1109/TSTE.2021.3077001}, abstractNote={We propose a two-stage probabilistic solar power (SP) forecasting algorithm to utilize the solar irradiation (SI) observations measured from different locations. In the first stage, we predict the SI based on the numerical weather prediction (NWP) after interpolating SI observations. Since the SI on the target location is not measured, we interpolate it using the spatio-temporal Kriging technique based on the SI observed from nearby weather stations. In the second stage, we forecast the SP based on the SI predictions after training the SI and SP observations. The model is trained by observations, but it forecasts based on predictions. Furthermore, in the two-stage model, forecasting errors can propagate across stages. We overcome these problems by using probabilistic forecasting. We design distributions of SI predictions through the probabilistic graphical model. Then, we extract SI scenarios from the distributions and predict SP scenarios based on these SI scenarios. We also group the NWP with respect to its prediction time, and we subdivide these groups as subgroups with respect to weather conditions. Furthermore, we propose a changeable ensemble model, where we have different weights for each weather condition. We verify our algorithm based on the data from the Korea power exchange renewable energy forecasting competition 2019. We finished the competition in 2nd place among a few hundred participants.}, number={4}, journal={IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}, author={Kim, Hyeonjin and Lee, Duehee}, year={2021}, month={Oct}, pages={2031–2041} }