scholarly journals Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

2020 ◽  
Vol 10 (8) ◽  
pp. 2962
Author(s):  
Lijuan Liu ◽  
Rung-Ching Chen ◽  
Shunzhi Zhu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Weather Conditions ◽  
Urban Transport ◽  
Weather Data ◽  
Weather Variables ◽  
Short Term ◽  
Passenger Flow ◽  
Input Variables ◽  
Main Station

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals A neural network model for prediction of deoxynivalenol content in wheat grain based on weather data and preceding crop

2008 ◽  
Vol 53 (No. 10) ◽  
pp. 421-429 ◽  
Author(s):  
K. Klem ◽  
M. Váňová ◽  
J. Hajšlová ◽  
K. Lancová ◽  
M. Sehnalová
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Field Experiments ◽  
Weather Conditions ◽  
Weather Data ◽  
Continuous Variables ◽  
Wheat Grain ◽  
Preceding Crop ◽  
Input Variables

Deoxynivalenol (DON) is the most prevalent Fusarium toxin in Czech wheat samples and therefore forecasting this mycotoxin is a potentially useful tool to prevent it from entering into food chain. The data about DON content in wheat grain, weather conditions during the growing season and cultivation practices from two field experiments conducted in 2002–2005 were used for the development of neural network model designed for DON content prediction. The winning neural network is based on five input variables: a categorial variable – preceding crop, and continuous variables – average April temperature, sum of April precipitation, average temperature 5 days prior to anthesis, sum of precipitation 5 days prior to anthesis. The most important input parameters are the preceding crop and sum of precipitation 5 days prior to anthesis. The weather conditions in April, which are important for inoculum formation on crop debris are also of important contribution to the model. The weather conditions during May and 5 days after anthesis play only an insignificant role for the DON content in grain. The effect of soil cultivation was found inferior for model function as well. The correlation between observed and predicted data using the neural network model reached the coefficient <i>R</i><sup>2</sup> = 0.87.

scholarly journals Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Hla U May Marma ◽  
M. Tariq Iqbal ◽  
Christopher Thomas Seary
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Power Load ◽  
Power Load Forecasting ◽  
Long Short Term Memory

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour.  In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

scholarly journals Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fang Zhao ◽  
Ziyi Liang ◽  
Qiyan Zhang ◽  
Dewen Seng ◽  
Xiyuan Chen
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Short Term Memory ◽  
Environmental Data ◽  
Weather Data ◽  
Short Term ◽  
Concentration Data ◽  
Data Set ◽  
Term Memory ◽  
Proposed Model

Accurate monitoring of air quality can no longer meet people’s needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models.

scholarly journals Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 718 ◽  
Author(s):  
Park ◽  
Kim ◽  
Lee ◽  
Kim ◽  
Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Short Term Memory ◽  
Weather Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

scholarly journals A Machine Learning Approach for Anaerobic Reactor Performance Prediction Using Long Short-Term Memory Recurrent Neural Network

2021 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Z Score ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Input Variables

Abstract. Predictive models are important to help manage high-value assets and to ensure optimal and safe operations. Recently, advanced machine learning algorithms have been applied to solve practical and complex problems, and are of significant interest due to their ability to adaptively ‘learn’ in response to changing environments. This paper reports on the data preparation strategies and the development and predictive capability of a Long Short-Term Memory recurrent neural network model for anaerobic reactors employed at Melbourne Water’s Western Treatment Plant for sewage treatment that includes biogas harvesting. The results show rapid training and higher accuracy in predicting biogas production when historical data, which include significant outliers, are preprocessed with z-score standardisation in comparison to those with max-min normalisation. Furthermore, a trained model with a reduced number of input variables via the feature selection technique based on Pearson’s correlation coefficient is found to yield good performance given sufficient dataset training. It is shown that the overall best performance model comprises the reduced input variables and data processed with z-score standardisation. This initial study provides a useful guide for the implementation of machine learning techniques to develop smarter structures and management towards Industry 4.0 concepts.

scholarly journals Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 32 ◽  
Author(s):  
Ke Yan ◽  
Hengle Shen ◽  
Lei Wang ◽  
Huiming Zhou ◽  
Meiling Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solar Irradiance ◽  
Power Plants ◽  
Short Term Memory ◽  
Learning Model ◽  
Weather Data ◽  
Short Term ◽  
Original Dataset ◽  
Deep Learning Model

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).

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