894 research outputs found
Epidemiological Prediction using Deep Learning
Department of Mathematical SciencesAccurate and real-time epidemic disease prediction plays a significant role in the health system and is of great importance for policy making, vaccine distribution and disease control. From the SIR model by Mckendrick and Kermack in the early 1900s, researchers have developed a various mathematical model to forecast the spread of disease. With all attempt, however, the epidemic prediction has always been an ongoing scientific issue due to the limitation that the current model lacks flexibility or shows poor performance. Owing to the temporal and spatial aspect of epidemiological data, the problem fits into the category of time-series forecasting. To capture both aspects of the data, this paper proposes a combination of recent Deep Leaning
models and applies the model to ILI (influenza like illness) data in the United States. Specifically, the graph convolutional network (GCN) model is used to capture the geographical feature of the U.S. regions and the gated recurrent unit (GRU) model is used to capture the temporal dynamics of ILI. The result was compared with the Deep Learning model proposed by other researchers, demonstrating the proposed model outperforms the previous methods.clos
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.Comment: 39 pages, 10 figure
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
For short-term solar irradiance forecasting, the traditional point
forecasting methods are rendered less useful due to the non-stationary
characteristic of solar power. The amount of operating reserves required to
maintain reliable operation of the electric grid rises due to the variability
of solar energy. The higher the uncertainty in the generation, the greater the
operating-reserve requirements, which translates to an increased cost of
operation. In this research work, we propose a unified architecture for
multi-time-scale predictions for intra-day solar irradiance forecasting using
recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
This paper also lays out a framework for extending this modeling approach to
intra-hour forecasting horizons thus, making it a multi-time-horizon
forecasting approach, capable of predicting intra-hour as well as intra-day
solar irradiance. We develop an end-to-end pipeline to effectuate the proposed
architecture. The performance of the prediction model is tested and validated
by the methodical implementation. The robustness of the approach is
demonstrated with case studies conducted for geographically scattered sites
across the United States. The predictions demonstrate that our proposed unified
architecture-based approach is effective for multi-time-scale solar forecasts
and achieves a lower root-mean-square prediction error when benchmarked against
the best-performing methods documented in the literature that use separate
models for each time-scale during the day. Our proposed method results in a
71.5% reduction in the mean RMSE averaged across all the test sites compared to
the ML-based best-performing method reported in the literature. Additionally,
the proposed method enables multi-time-horizon forecasts with real-time inputs,
which have a significant potential for practical industry applications in the
evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio
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Building thermal load prediction through shallow machine learning and deep learning
Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty
Water Demand Forecasting using Deep Learning in IoT Enabled Water Distribution Network
Most of the water losses occur during water distribution in pipelines during transportation. In order to eradicate the losses, an “IoT based water distribution system” integrated with “Fog and Cloud Computing" proposed for water distribution and underground health monitoring of pipes. For developing an effective water distribution system based on Internet of Things (IoT), the demand of the consumer should be analysed. So, towards predicting the water demand for consumers, Deep learning methodology called Long Short-Term Memory (LSTM) is compared with traditional Time Series methodology called Auto Regressive Integrated Moving Average (ARIMA) in terms of error and accuracy. Now based on demand prediction with higher accuracy, an IoT integrated “Water Distribution Network (WDN)” is designed using hydraulic engineering. This WDN design will ensure minimal losses during transportation and quality of water to the consumers. This will lead to development of a smart system for water distribution
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