Short-Term Load Forecasting for an individual building using Shape-based Clustering and Random Forest

Recently, ICT-based smart grid-related businesses have been increasing as distributed energy resources are expanding. The load forecasting is one of the key technologies for the efficient operation of the businesses, so many related studies have been published. However, since loads of individual buildings are more volatile than a large-scale load in general, forecasting individual consumers’ loads is much more challenging and only limited studies have been published. In this paper, we propose a hybrid method to forecast electricity loads of individual buildings in a day-ahead manner. Using DTW similarities in load profiles was calculated focusing on their shapes, and clustering is conducted for pattern recognition. We estimate the pattern for the next day by random forest, and combined historical loads with weather information to forecast the hourly load of the day. For performance evaluation, 1,065 days of building load data were tested. The clustering method in our study provided better quality clusters and the classification model outperformed the benchmark model. Also, the hybrid structure recorded high prediction accuracy compared to a single structure.22Nkc

추계적 계획법을 통한 태양광 분산자원 전력중개 최적 입찰량 결정 연구

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불확실성을 고려한 에너지 시스템 구축 및 온실가스 감축 목표 달성 가능성 정량화

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Optimal bidding strategy for virtual power plant utilizing multi-stage stochastic dynamic programming

Virtual power plants(VPPs) are being deployed owing to the increase of reliance on distributed energy resources (DERs) as well as the development in the energy storage system (ESS). In this study, we propose an optimal bidding strategy model for VPPs in a day-ahead electricity market. This strategic bidding model aims to maximize the expected profit of VPP, taking into account the uncertainties in demand and DER generation. By generating the scenario tree of forecast error, we quantify the uncertain factors. Finally, the problem is modeled as the multi-stage stochastic dynamic program where the bidding decision is made in the first stage and the operation of ESS in the remaining stages. The effectiveness of the proposed strategy has been assessed on a real case study.1

Synergistic Antibiotic Activity of Ricini Semen Extract with Oxacillin against Methicillin-Resistant <i>Staphylococcus aureus</i>

Resistant bacteria are emerging as a critical problem in the treatment of bacterial infections by neutralizing antibiotic activity. The development of new traditional mechanisms of antibiotics is not the optimal solution. A more reasonable approach may be to use relatively safe, plant-based compounds in combination with conventional antibiotics in an effort to increase their efficacy or restore their activity against resistant bacteria. We present our study of mixing Ricini Semen extract, or its constituent fatty acids, with oxacillin and testing the effects of each on the growth of methicillin-resistant Staphylococcus aureus. Changes in the cell membrane fluidity of methicillin-resistant S. aureus were found to be a major component of the mechanism of synergistic antibiotic activity of Ricini Semen extract and its constituent fatty acids. In our model, changes in cellular membrane fluidity disrupted the normal function of bacterial signaling membrane proteins BlaR1 and MecR1, which are known to detect oxacillin, and resulted in the incomplete expression of penicillin-binding proteins 2a and β-lactamase. Utilizing the mechanism presented in this study presents the possibility of developing a method for treating antibiotic-resistant bacteria using traditional antibiotics with plant-based compounds