Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting.

In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour meter, accurate price prediction becomes more and more crucial in the energy management and control of energy storage systems. Due to the great uncertainty of electricity price, the performance of the general electricity price forecasting models is not satisfactory to be adopted in practice. Therefore, in this paper, we propose a novel electricity price forecasting strategy applied in optimization for the scheduling of battery energy storage systems. At first, multiple nonstationary decompositions are presented to extract the most significant components in price series, which express remarkably discriminative features in price fluctuation for regression prediction. In addition, all extracted components are delivered to a devised deep convolution neural network with multiscale dilated kernels for multistep price forecasting. At last, more advanced price fluctuation detection serves the optimized operation of the battery energy storage system within Ontario grid-connected microgrids. Sufficient ablation studies showed that our proposed price forecasting strategy provides predominant performances compared with the state-of-the-art methods and implies a promising prospect in economic benefits of battery energy storage systems.

Long non-coding RNA NKILA serves as a biomarker in the early diagnosis and prognosis of patients with colorectal cancer.

Colorectal cancer (CRC) is one of the leading causes of cancer-associated mortality worldwide. The prognosis of patients with CRC at an advanced stage is poor. Biomarkers currently used in clinical practice, including carcinoembryonic antigen (CEA) and cancer antigen (CA) 19-9, lack sufficient sensitivity and specificity for early diagnosis and prediction, therefore there remains a requirement to improve the prognosis of patients with CRC. Long non-coding RNAs (lncRNAs) have been revealed to serve fundamental roles in various pathophysiological processes, including cancer initiation and progression. The present study investigated the expression and clinical significance of the lncRNA nuclear factor-κB interacting long non-coding RNA (NKILA) in CRC. It was identified that NKILA was downregulated in six CRC cell lines and tissues (n=173). Low NKILA expression was significantly associated with a poor differentiation grade, larger tumor size and advanced Tumor-Node-Metastases stages. Further statistical analyses revealed that low NKILA expression predicted poor overall survival (OS) rate and progression-free survival (PFS) rate. In addition, low NKILA expression was determined as an independent risk factor for poor OS and PFS. Furthermore, NKILA exhibited a relatively high sensitivity and specificity compared with CEA and CA19-9 in the early diagnosis of CRC. The serum level of NKILA was positively correlated with the level in tissues. In addition, a decreased NKILA level in serum was revealed to be partially restored post-operatively. In conclusion, low NKILA expression has been demonstrated to accelerate CRC progression and NKILA may be a potential novel biomarker in early diagnosis and prognosis of patients with CRC.