In this paper, a characteristic load decomposition (CLD)-based day-ahead load forecasting scheme is proposed for a mixed-use complex. The aggregated load of the complex is composed of the mixtures of different electricity usage patterns, and short-term load forecasting can be implemented by summing disaggregated sub-load predictions. However, tracing all usage patterns of sub-loads for prediction may be infeasible because of limited resources for measurement and analysis. To prevent this infeasibility, the proposed scheme focuses on effective decomposition using the sub-loads of typical characteristic load profiles and their representative pilot signals. Separate forecasts are obtained for the decomposed characteristic sub-loads using a hybrid scheme, which combines day-type conditioned linear prediction with long short-term memory regressions. Complex campus load data are considered to evaluate the proposed CLD-based hybrid forecasting. The evaluation results show that the proposed scheme outperforms conventional hybrid or similar-day-based forecasting approaches. Even when sub-load measurements are available only for a limited period, the CLD scheme can be applied for the extended training data through virtual disaggregations. INDEX TERMS Day-ahead load forecasting, time series analysis, long short-term memory, hybrid forecasting model, characteristic load decomposition, hierarchical load forecasting.

Background/Purpose Pancreatic ductal adenocarcinoma (PDAC) is regarded as incurable, with a limited survival rate after curative resection. The aim of this study was to explore long‐term survival and late recurrence of PDAC after surgery. Methods Medical data of 859 patients who underwent resection between 1995 and 2014 were retrospectively examined. The clinicopathological features of the 5‐year recurrence‐free survivors and the patients with recurrent disease after 5 years were investigated separately. Results Among the 768 patients who were finally included in this study, elevated CA 19‐9, tumor size, poor differentiation, and positive lymph node metastasis were associated with recurrence. In 89 patients with 5‐year RFS, age, tumor size, differentiation, and lymph node metastasis were statistically significant predictive factors. Among these patients, disease relapse occurred in 11 patients; age was the only difference compared to those who remained free of recurrence. Conclusions Most prognosticators failed to predict the risk of recurrence in the 5 years following surgery for PDAC, and recurrence can occur even at time points up to 100 months. Therefore, cure of PDAC cannot be guaranteed by a 5‐year recurrence‐free interval, and further studies into the inherent nature of PDAC are needed to develop adequate surveillance systems which may lead to improvements in survival.

Biodegradable polymers and the hydrogels have been increasingly applied in a variety of biomedical fields and pharmaceutics. ␣,␤-Poly(N-2-hydroxyethyl-dl-aspartamide), PHEA, one of poly(amino acid)s with hydroxyethyl pendants, are known to be biodegradable and biocompatible, and has been studied as an useful biomaterial, especially for drug delivery, via appropriate structural modification. In this work, hydrogels based on PHEA were prepared by two-step reaction, that is, the crosslinking of polysuccinimide, the precursor polymer, with oligomeric PEG or PEI-diamines and the following nucleophilic ringopening reaction by ethanolamine. Soft hydrogels possessing varying degrees of gel strength could be prepared easily, depending on the amount of different crosslinking reagents.The swelling degrees, which were in the range of 10 -40 g-water/dry gel, increased somewhat at higher temperature, and also at alkaline pH of aqueous solution. A typical hydrogel remained almost unchanged for 1 week, at 37°C in phosphate buffer of pH 7.4, and then seemed to degrade slowly as time. A porous scaffold could be fabricated by the freeze drying of water-swollen gel. The PHEA-based hydrogels have potential for useful biomaterial applications including current drug delivery system.

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With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

In this paper, we propose a deep reinforcement learning (DRL) based predictive control scheme for reducing the energy consumption and energy cost of pumping systems in wastewater treatment plants (WWTP), in which the pumps are operated in a binary mode, using on/off signals. As global energy consumption increases, the efficient operation of energy-intensive facilities has also become important. A WWTP in Busan, Republic of Korea is used as the target of this study. This WWTP is a large energyconsuming facility, and the pumping station accounts for a significant portion of the energy consumption of the WWTP. The framework of the proposed scheme consists of a deep neural network (DNN) model for forecasting wastewater inflow and a DRL agent for controlling the on/off signals of the pumping system, where proximal policy optimization (PPO) and deep Q-neural network (DQN) are employed as the DRL agents. To implement smart control with DRL, a reward function is designed to consider the energy consumption amount and electricity price information. In particular, new features and penalty factors for pump switching, which are essential for preventing pump wear, are also considered. The performance of our designed DRL agents is compared with those of WWTP experts and conventional approaches such as scheduling method and model predictive control (MPC), in which integer linear programming (ILP) optimization is employed. Results show that the designed agents outperform the other approaches in terms of compliance with operating rules and reducing energy costs.

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