Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.
This report focuses on the thermo-sensitive liposomes loaded with doxorubicin and lysine-modified single-walled carbon nanotube drug delivery system, which was designed to enhance the anti-tumor effect and reduce the side effects of doxorubicin. Doxorubicin-lysine/single-walled carbon nanotube-thermo-sensitive liposomes was prepared by reverse-phase evaporation method, the mean particle size was 232.0 ± 5.6 nm, and drug entrapment efficiency was 86.5 ± 3.7%. The drug release test showed that doxorubicin released more quickly at 42℃ than at 37℃. Compared with free doxorubicin, doxorubicin-lysine/single-walled carbon nanotube-thermo-sensitive liposomes could efficiently cross the cell membranes and afford higher anti-tumor efficacy on the human hepatic carcinoma cell line (SMMC-7721) cells in vitro. For in vivo experiments, the relative tumor volumes of the sarcomaia 180-bearing mice in thermo-sensitive liposomes group and doxorubicin group were significantly smaller than those of N.S. group. Meanwhile, the combination of near-infrared laser irradiation at 808 nm significantly enhanced the tumor growth inhibition both on SMMC-7721 cells and the sarcomaia 180-bearing mice. The quality of life such as body weight, mental state, food and water intake of sarcomaia 180 tumor-bearing mice treated with doxorubicin-lysine/single-walled carbon nanotube-thermo-sensitive liposomes were much higher than those treated with doxorubicin. In conclusion, doxorubicin-lysine/single-walled carbon nanotube-thermo-sensitive liposomes combined with near-infrared laser irradiation at 808 nm may potentially provide viable clinical strategies for targeting delivery of anti-cancer drugs.
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