Neural network models for electricity prices and loads short and long-term prediction

2016 ◽  
Author(s):  
Dimitrije Kotur ◽  
Mileta Zarkovic
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Electricity Prices ◽  
Neural Network Models ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction

scholarly journals DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index

2020 ◽  
Vol 10 (4) ◽  
pp. 1504 ◽  
Author(s):  
Imam Mustafa Kamal ◽  
Hyerim Bae ◽  
Sim Sunghyun ◽  
Heesung Yun
Keyword(s):  
Deep Learning ◽  
Network Models ◽  
Neural Network Models ◽  
Business Decisions ◽  
Term Prediction ◽  
Short And Long Term ◽  
The Baltic ◽  
Long Term Prediction ◽  
Baltic Dry Index

The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

A spiking neural network-based long-term prediction system for biogas production

2020 ◽  
Vol 129 ◽  
pp. 271-279 ◽  
Author(s):  
Giacomo Capizzi ◽  
Grazia Lo Sciuto ◽  
Christian Napoli ◽  
Marcin Woźniak ◽  
Gianluca Susi
Keyword(s):  
Neural Network ◽  
Biogas Production ◽  
Spiking Neural Network ◽  
Prediction System ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6213
Author(s):  
Anjan Rao Puttige ◽  
Staffan Andersson ◽  
Ronny Östin ◽  
Thomas Olofsson
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Analytical Model ◽  
Hybrid Model ◽  
Time Resolution ◽  
Network Models ◽  
Borehole Heat Exchanger ◽  
Neural Network Models ◽  
Ground Source

Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.

scholarly journals Serotonin Differentially Regulates Short- and Long-Term Prediction of Rewards in the Ventral and Dorsal Striatum

PLoS ONE ◽  
2007 ◽  
Vol 2 (12) ◽  
pp. e1333 ◽  
Author(s):  
Saori C. Tanaka ◽  
Nicolas Schweighofer ◽  
Shuji Asahi ◽  
Kazuhiro Shishida ◽  
Yasumasa Okamoto ◽  
...  
Keyword(s):  
Dorsal Striatum ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction

O4-04-05: Differences in short- and long-term prediction of dementia in a 15-year longitudinal population study

2006 ◽  
Vol 2 ◽  
pp. S83-S83
Author(s):  
Simona F. Sacuiu ◽  
Boo Johansson ◽  
Svante Östling ◽  
Deborah Gustafson ◽  
Ingmar Skoog
Keyword(s):  
Population Study ◽  
Term Prediction ◽  
Short And Long Term ◽  
Long Term Prediction

scholarly journals Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Tushar Saini ◽  
Pratik Chaturvedi ◽  
Varun Dutt
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Network Models ◽  
Economic Losses ◽  
Ensemble Model ◽  
Neural Network Models ◽  
Ensemble Techniques ◽  
Bidirectional Lstm

Air quality is a major problem in the world, having severe health implications. Long-term exposure to poor air quality causes pulmonary and cardiovascular diseases. Several studies have also found that deteriorating air quality also causes substantial economic losses. Thus, techniques that can forecast air quality with higher accuracy may help reduce health and economic consequences. Prior research has utilized state-of-the-art artificial neural network and recurrent neural network models for forecasting air quality. However, a comprehensive investigation of different architectures of recurrent neural network, especially LSTMs and ensemble techniques, has been less explored. Also, there have been less explorations of long-term air quality forecasts via these methods exists. This research proposes the development and calibration of recurrent neural network models and their ensemble, which can forecast air quality in terms of PM2.5 concentration 6 hours ahead in time. For forecasting air quality, a vanilla-LSTM, a stack-LSTM, a bidirectional-LSTM, a CNN-LSTM, and an ensemble of individual LSTM models were trained on the UCI Machine Learning Beijing dataset. Data were split into two parts, where 80% of data were used for training the models, while the remaining 20% were used for validating the models. For comparative analysis, four regression losses were calculated, namely root mean squared error, mean absolute percentage error, mean absolute error and Pearson’s correlation coefficient. Results revealed that among all models, the ensemble model performed the best in predicting the PM2.5 concentrations. Furthermore, the ensemble model outperformed other models reported in literature by a long margin. Among the individual models, the bidirectional-LSTM performed the best. We highlight the implications of this research on long-term forecasting of air quality via recurrent and ensemble techniques.

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