Deep Learning Network for Energy Storage Scheduling in Power Market Environment Short-Term Load Forecasting Model

In the electricity market,fluctuations in real-time prices are unstable,and changes in short-term load are determined by many factors.By studying the timing of charging and discharging,as well as the economic benefits of energy storage in the process of participating in the power market,this paper takes energy storage scheduling as merely one factor affecting short-term power load,which affects short-term load time series along with time-of-use price,holidays,and temperature.A deep learning network is used to predict the short-term load,a convolutional neural network(CNN)is used to extract the features,and a long short-term memory(LSTM)network is used to learn the temporal characteristics of the load value,which can effectively improve prediction accuracy.Taking the load data of a certain region as an example,the CNN-LSTM prediction model is compared with the single LSTM prediction model.The experimental results show that the CNN-LSTM deep learning network with the participation of energy storage in dispatching can have high prediction accuracy for short-term power load forecasting.

Fusion of Spiral Convolution-LSTM for Intrusion Detection Modeling

Aiming at the problems of low accuracy and slow convergence speed of current intrusion detection models,SpiralConvolution is combined with Long Short-Term Memory Network to construct a new intrusion detection model.The dataset is first preprocessed using solo thermal encoding and normalization functions.Then the spiral convolution-Long Short-Term Memory Network model is constructed,which consists of spiral convolution,a two-layer long short-term memory network,and a classifier.It is shown through experiments that the model is characterized by high accuracy,small model computation,and fast convergence speed relative to previous deep learning models.The model uses a new neural network to achieve fast and accurate network traffic intrusion detection.The model in this paper achieves 0.9706 and 0.8432 accuracy rates on the NSL-KDD dataset and the UNSWNB-15 dataset under five classifications and ten classes,respectively.

Hourly traffic flow forecasting using a new hybrid modelling method

Short-term traffic flow forecasting is a significant part of intelligent transportation system.In some traffic control scenarios,obtaining future traffic flow in advance is conducive to highway management department to have sufficient time to formulate corresponding traffic flow control measures.In hence,it is meaningful to establish an accurate short-term traffic flow method and provide reference for peak traffic flow warning.This paper proposed a new hybrid model for traffic flow forecasting,which is composed of the variational mode decomposition(VMD)method,the group method of data handling(GMDH)neural network,bi-directional long and short term memory(BILSTM)network and ELMAN network,and is optimized by the imperialist competitive algorithm(ICA)method.To illustrate the performance of the proposed model,there are several comparative experiments between the proposed model and other models.The experiment results show that 1)BILSTM network,GMDH network and ELMAN network have better predictive performance than other single models;2)VMD can significantly improve the predictive performance of the ICA-GMDH-BILSTM-ELMAN model.The effect of VMD method is better than that of EEMD method and FEEMD method.To conclude,the proposed model which is made up of the VMD method,the ICA method,the BILSTM network,the GMDH network and the ELMAN network has excellent predictive ability for traffic flow series.

Performance of U-tube Solar Water Heaters Facing Different Directions

System performance of solar water heaters depends upon collector and storage tank designs, solar radiation intensity and ambient temperature, amongst others. Evacuated glass tube collectors with U-tubes inside are less prone to leakages than the all-glass or the heat pipe types. U-tube solar water heaters suspended on walls and balconies could help overcome present day roof space restriction and increasing apartment-style housing. As such, their performance would depend upon its orientation when mounted in a vertical position. This paper reports the results of outdoor tests conducted on natural convection U-tube solar water heaters oriented towards different directions. Long and short term test procedures were employed to allow us to compare their performances as if they were tested simultaneously side-by-side.

Finger millet porridges subjected to different processing conditions showed low glycemic index and variable efficacy on plasma antioxidant capacity of healthy adults

Finger millet porridges(FMP),rich in nutrient and non-nutrient compounds have been used in the traditional food cultures in Asia.The aims of the study were to determine the effect of different processing conditions of finger millet grains on glycemic response,phenolic content and the antioxidant activities of FMP and to determine the short term and long term efficacy of its consumption on plasma antioxidant levels of healthy adults.Twelve types of FMP were prepared combining different processing conditions.Phenolic content of porridges as well as antioxidant activities were determined.The glycemic index(GI)and glycemic load(GL)values of FMP were also evaluated.The long term efficacy of FMP consumption on plasma glucose(PG),total cholesterol(TC)levels and plasma antioxidant capacity(PAC)of 18 subjects were investigated using a 24 weeks randomized cross-over study.The short term efficacy of porridge consumption on AC was determined.PAC was measured by trolox equivalent antioxidant capacity(TEAC)and ferric ion reducing antioxidant power(FRAP).All FMP exhibited low GI values(<55)except the raw roasted flour which showed high and medium GI values for both particle sizes used.Parboiling of finger millet grains with 15 min steaming produced FMP with low glycemic response and possessed high PAC.Compared to baseline,PAC measured using FRAP and TEAC assays increased after 8 weeks consumption of porridge though significant changes were not observed for PG and TC levels.Furthermore,PAC was increased by 23 and 14%after 2 h of porridge consumption as measured by TEAC and FRAP,respectively.FMP consumption increased the plasma total antioxidant capacity of healthy adults.Further research on examining the potential of FMP on improving the antioxidant capacity in patients with diabetes is warranted.

A deep network prediction model for heavy metal cadmium in the rice supply chain

Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.