scholarly journals Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 744
Author(s):  
Elsa Chaerun Nisa ◽  
Yean-Der Kuan
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Power Consumption ◽  
Energy Demand ◽  
Prediction Models ◽  
Short Term Memory ◽  
Time Series Forecasting ◽  
Experimental Result ◽  
Forecasting Model

Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R2), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R2, 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value.

scholarly journals A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

2021 ◽  
Vol 5 (4) ◽  
pp. 175
Author(s):  
Ahmed I. Shahin ◽  
Sultan Almotairi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Learning Models ◽  
Forecasting Models ◽  
Proposed Model ◽  
Death Cases

The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 149 ◽  
Author(s):  
Salah Bouktif ◽  
Ali Fiaz ◽  
Ali Ouni ◽  
Mohamed Adel Serhani
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Time Lags ◽  
Electric Load ◽  
Learning Approaches ◽  
Electric Load Forecasting

Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a lack of validity because it neglects several characteristics of the electric load exhibited by time series. In this work, we propose a load-forecasting model based on enhanced-LSTM that explicitly considers the periodicity characteristic of the electric load by using multiple sequences of inputs time lags. An autoregressive model is developed together with an autocorrelation function (ACF) to regress consumption and identify the most relevant time lags to feed the multi-sequence LSTM. Two variations of deep neural networks, LSTM and gated recurrent unit (GRU) are developed for both single and multi-sequence time-lagged features. These models are compared to each other and to a spectrum of data mining benchmark techniques including artificial neural networks (ANN), boosting, and bagging ensemble trees. France Metropolitan’s electricity consumption data is used to train and validate our models. The obtained results show that GRU- and LSTM-based deep learning model with multi-sequence time lags achieve higher performance than other alternatives including the single-sequence LSTM. It is demonstrated that the new models can capture critical characteristics of complex time series (i.e., periodicity) by encompassing past information from multiple timescale sequences. These models subsequently achieve predictions that are more accurate.

scholarly journals Study of the impact of the COVID-19 pandemic on international air transportation

2021 ◽  
Vol 29 (1) ◽  
pp. 22-35
Author(s):  
Eugeny Yu. Shchetinin
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Stock Prices ◽  
Short Term Memory ◽  
Air Transportation ◽  
Time Series Forecasting ◽  
The Cost ◽  
The Impact

Time Series Forecasting has always been a very important area of research in many domains because many different types of data are stored as time series. Given the growing availability of data and computing power in the recent years, Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results.As different time series problems are studied in many different fields, a large number of new architectures have been developed in recent years. This has also been simplified by the growing availability of open source frameworks, which make the development of new custom network components easier and faster.In this paper three different Deep Learning Architecture for Time Series Forecasting are presented: Recurrent Neural Networks (RNNs), that are the most classical and used architecture for Time Series Forecasting problems; Long Short-Term Memory (LSTM), that are an evolution of RNNs developed in order to overcome the vanishing gradient problem; Gated Recurrent Unit (GRU), that are another evolution of RNNs, similar to LSTM.The article is devoted to modeling and forecasting the cost of international air transportation in a pandemic using deep learning methods. The author builds time series models of the American Airlines (AAL) stock prices for a selected period using LSTM, GRU, RNN recurrent neural networks models and compare the accuracy forecast results.

scholarly journals Development of a Model for Predicting the Direction of Daily Price Changes in the Forex Market Using Long Short-Term Memory

2021 ◽  
Vol 11 (1) ◽  
pp. 61-67
Author(s):  
Watthana Pongsena ◽  
◽  
Prakaidoy Sitsayabut ◽  
Nittaya Kerdprasop ◽  
Kittisak Kerdprasop ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Price Changes ◽  
Term Memory ◽  
The Future ◽  
Long Short Term Memory

Forex is the largest global financial market in the world. Traditionally, fundamental and technical analysis are strategies that the Forex traders often used. Nowadays, advanced computational technology, Artificial Intelligence (AI) has played a significant role in the financial domain. Various applications based on AI technologies particularly machine learning and deep learning have been constantly developed. As the historical data of the Forex are time-series data where the values from the past affect the values that will appear in the future. Several existing works from other domains of applications have proved that the Long-Short Term Memory (LSTM), which is a particular kind of deep learning that can be applied to modeling time series, provides better performance than traditional machine learning algorithms. In this paper, we aim to develop a powerful predictive model targeting to predicts the daily price changes of the currency pairwise in the Forex market using LSTM. Besides, we also conduct an extensive experiment with the intention to demonstrate the effect of various factors contributing to the performance of the model. The experimental results show that the optimized LSTM model accurately predicts the direction of the future price up to 61.25 percent.

scholarly journals Possibilities in the application of machine learning on bioimpedance time-series

2019 ◽  
Vol 10 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Christian Tronstad ◽  
Runar Strand-Amundsen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Prediction Models ◽  
Short Term Memory ◽  
Synthetic Data ◽  
Relevant Information ◽  
Published Data ◽  
Input Variables ◽  
Training Examples ◽  
Over Time

Abstract The relation between a biological process and the changes in passive electrical properties of the tissue is often non-linear, in which developing prediction models based on bioimpedance spectra is not trivial. Relevant information on tissue status may also lie in characteristic developments in the bioimpedance spectra over time, often neglected by conventional methods. The aim of this study was to explore possibilities in machine learning methods for time series of bioimpedance spectra, where we used organ ischemia as an example. Based on published data on the development of the bioimpedance spectrum during liver ischemia, a simulation model was made and used to generate sets of synthetic data with different levels of organ-to-organ variation, measurement noise and drift. Three types of artificial neural networks were employed in learning to predict the ischemic duration, based on the simulated datasets. The simulated prediction performance was very dependent on the amount of training examples, the organ-to-organ variation and the selection of input variables from the bioimpedance spectrum. The performance was also affected by noise and drift in the measurement, but a recurrent neural network with long short-term memory units could obtain good predictions even on noisy and drifting measurements. This approach may be relevant for further exploration on several applications of bioimpedance having the purpose of predicting a biological state based on spectra measured over time.

scholarly journals Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

2020 ◽  
Vol 10 (7) ◽  
pp. 2322 ◽  
Author(s):  
Pedro Lara-Benítez ◽  
Manuel Carranza-García ◽  
José M. Luna-Romera ◽  
José C. Riquelme
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Demand ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Time Series Forecasting ◽  
Series Data ◽  
Recurrent Networks ◽  
Convolutional Networks ◽  
Related Time Series

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

A novel time series forecasting model with deep learning

2020 ◽  
Vol 396 ◽  
pp. 302-313 ◽  
Author(s):  
Zhipeng Shen ◽  
Yuanming Zhang ◽  
Jiawei Lu ◽  
Jun Xu ◽  
Gang Xiao
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Forecasting Model

Modern technologies of deep learning for forecasting time series

2021 ◽  
pp. 95-105
Author(s):  
А.И. Сотников
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Time Series Forecasting ◽  
Machine Learning Methods ◽  
Modern Technologies

Прогнозирование временных рядов стало очень интенсивной областью исследований, число которых в последние годы даже увеличивается. Глубокие нейронные сети доказали свою эффективность и достигают высокой точности во многих областях применения. По этим причинам в настоящее время они являются одним из наиболее широко используемых методов машинного обучения для решения проблем, связанных с большими данными. Time series forecasting has become a very intensive area of research, the number of which has even increased in recent years. Deep neural networks have been proven to be effective and achieve high accuracy in many applications. For these reasons, they are currently one of the most widely used machine learning methods for solving big data problems.

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