scholarly journals Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3072 ◽  
Author(s):  
Dazhou Li ◽  
Chuan Lin ◽  
Wei Gao ◽  
Zihui Meng ◽  
Qi Song
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Public Transportation ◽  
Smart City ◽  
Tensor Decomposition ◽  
Multiple Time ◽  
Short Term ◽  
Last Mile ◽  
Source Data ◽  
Higher Order Tensors

As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the “last mile” of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

scholarly journals A Markov Regime Switching Model for Ultra-Short-Term Wind Power Prediction Based on Toeplitz Inverse Covariance Clustering

2021 ◽  
Vol 9 ◽  
Author(s):  
Hang Fan ◽  
Xuemin Zhang ◽  
Shengwei Mei ◽  
Junzi Zhang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Wind Power ◽  
Regime Switching ◽  
Wind Farms ◽  
Power Prediction ◽  
Short Term ◽  
Markov Regime Switching ◽  
Switching Model ◽  
Switching Method

The rapid development of wind energy has brought a lot of uncertainty to the power system. The accurate ultra-short-term wind power prediction is the key issue to ensure the stable and economical operation of the power system. It is also the foundation of the intraday and real-time electricity market. However, most researches use one prediction model for all the scenarios which cannot take the time-variant and non-stationary property of wind power time series into consideration. In this paper, a Markov regime switching method is proposed to predict the ultra-short-term wind power of multiple wind farms. In the regime switching model, the time series is divided into several regimes that represent different hidden patterns and one specific prediction model can be designed for each regime. The Toeplitz inverse covariance clustering (TICC) is utilized to divide the wind power time series into several hidden regimes and each regime describes one special spatiotemporal relationship among wind farms. To represent the operation state of the wind farms, a graph autoencoder neural network is designed to transform the high-dimensional measurement variable into a low-dimensional space which is more appropriate for the TICC method. The spatiotemporal pattern evolution of wind power time series can be described in the regime switching process. Markov chain Monte Carlo (MCMC) is used to generate the time series of several possible regime numbers. The Kullback-Leibler (KL) divergence criterion is used to determine the optimal number. Then, the spatiotemporal graph convolutional network is adopted to predict the wind power for each regime. Finally, our Markov regime switching method based on TICC is compared with the classical one-state prediction model and other Markov regime switching models. Tests on wind farms located in Northeast China verified the effectiveness of the proposed method.

scholarly journals Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

2020 ◽  
Vol 185 ◽  
pp. 01052
Author(s):  
Runjie Shen ◽  
Ruimin Xing ◽  
Yiying Wang ◽  
Danqiong Hua ◽  
Ming Ma
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Random Forest ◽  
Prediction Model ◽  
Power Plants ◽  
Time Series Prediction ◽  
Cloud Amount ◽  
Continuous Model ◽  
Short Term ◽  
Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

scholarly journals Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region

2022 ◽  
Vol 18 (2) ◽  
pp. 198-223
Author(s):  
Farin Cyntiya Garini ◽  
Warosatul Anbiya
Keyword(s):  
Time Series ◽  
Public Transportation ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Method ◽  
Short Term ◽  
Seasonal Factors ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Over Time

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.

A Time Series Prediction Model Based on Long Short-Term Memory Networks

2021 ◽  
Author(s):  
Linkai Wang ◽  
Jing Chen ◽  
Wei Wang ◽  
Ruofan Wang ◽  
Lina Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Long Short Term Memory

Short-Term Reactive Power Load Forecasting Using Multiple Time-Series Model

2003 ◽  
Vol 36 (20) ◽  
pp. 985-990 ◽  
Author(s):  
Jong-Man Cho ◽  
Jin-Hack Kim ◽  
Woo-Hyun Park ◽  
Yun-ho Lee ◽  
Jin-O Kim
Keyword(s):  
Time Series ◽  
Reactive Power ◽  
Load Forecasting ◽  
Time Series Model ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting

scholarly journals Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 941
Author(s):  
Seongyoep Jeong ◽  
Inyoung Park ◽  
Hyun Soo Kim ◽  
Chul Han Song ◽  
Hong Kook Kim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Image Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Long Short Term Memory

Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS). To accommodate such different types of data into a single model, a bidirectional long short-term memory (BLSTM) model and a convolutional neural network (CNN) model are chosen to represent the features from the time-series observed data and the RDAPS image data. The two types of features are combined to produce temperature predictions for up to 14 days in the future. The performance of the proposed temperature prediction model is evaluated by objective measures, including the root mean squared error and mean bias error. The experiments demonstrated that the proposed model combining both the observed and RDAPS image data is better in all performance measures for all prediction periods compared with the BLSTM-based model using observed data and the CNN-BLSTM-based model using RDAPS image data alone.

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