scholarly journals Minutely Active Power Forecasting Models Using Neural Networks

2020 ◽  
Vol 12 (8) ◽  
pp. 3177 ◽  
Author(s):  
Dimitrios Kontogiannis ◽  
Dimitrios Bargiotas ◽  
Aspassia Daskalopulu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Power Systems ◽  
Demand Response ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Real World Data ◽  
Training Time ◽  
Power Forecasting

Power forecasting is an integral part of the Demand Response design philosophy for power systems, enabling utility companies to understand the electricity consumption patterns of their customers and adjust price signals accordingly, in order to handle load demand more effectively. Since there is an increasing interest in real-time automation and more flexible Demand Response programs that monitor changes in the residential load profiles and reflect them according to changes in energy pricing schemes, high granularity time series forecasting is at the forefront of energy and artificial intelligence research, aimed at developing machine learning models that can produce accurate time series predictions. In this study we compared the baseline performance and structure of different types of neural networks on residential energy data by formulating a suitable supervised learning problem, based on real world data. After training and testing long short-term memory (LSTM) network variants, a convolutional neural network (CNN), and a multi-layer perceptron (MLP), we observed that the latter performed better on the given problem, yielding the lowest mean absolute error and achieving the fastest training time.

scholarly journals Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1168
Author(s):  
Wenxiang Guo ◽  
Xiyu Liu ◽  
Laisheng Xiang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Anomaly Detection ◽  
Domain Knowledge ◽  
Time Series Data ◽  
Short Term Memory ◽  
Membrane System ◽  
Superior Performance ◽  
Series Data ◽  
Training Time

Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.

scholarly journals Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4733
Author(s):  
Andi A. H. Lateko ◽  
Hong-Tzer Yang ◽  
Chao-Ming Huang ◽  
Happy Aprillia ◽  
Che-Yuan Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Weather Data ◽  
Support Vector ◽  
Short Term ◽  
Learner Model ◽  
Average Maximum ◽  
Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

scholarly journals Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7426
Author(s):  
Imene Mitiche ◽  
Tony McGrail ◽  
Philip Boreham ◽  
Alan Nesbitt ◽  
Gordon Morison
Keyword(s):  
Anomaly Detection ◽  
High Voltage ◽  
Short Term Memory ◽  
Absolute Error ◽  
Real World Data ◽  
Voltage Transformer ◽  
Power Outage ◽  
Phase Measurements ◽  
High Voltage Transformer ◽  
Supply Industry

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

2019 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Training Time ◽  
Important Solution ◽  
Reverberant Speech

Speech signals are degraded in real-life environments, product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions.To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long and short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combination of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation has been made based on quality measurements of the signal's spectrum, training time of the networks and statistical validation of results. Results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, with advantages in efficiency, but without a significan drop in quality.

Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting

2020 ◽  
Vol 30 (08) ◽  
pp. 2050039 ◽  
Author(s):  
Foued Saâdaoui ◽  
Othman Ben Messaoud
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Empirical Mode Decomposition ◽  
Multivariate Time Series ◽  
Seasonal Effects ◽  
Real World Data ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Mode Decomposition ◽  
Nonlinear Trends

Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-based Neural ARDL, is inspired from the well-known Autoregressive Distributed Lag (ARDL) model being our proposal founded upon the concepts of nonlinearity, EMD-multiresolution and neural networks. These features give the model the ability to effectively capture many nonlinear patterns like the ones often present in econophysical time series, such as nonlinear trends, seasonal effects, long-range dependency, etc. The proposed algorithm can be summarized into the following four basic tasks: (i) EMD breaking-down multivariate time series into different resolution levels, (ii) feeding EMD components from the same levels into a number of feedforward neural ARDL models, (iii) from one level to the next, extrapolating the component corresponding to the response variable (scalar output) a number of steps ahead, and finally, (iv) recombining level-by-level forecasts into a single output. An optimal learning scheme is rigorously designed for efficiently training the new proposed architecture. The approach is finally tested and compared to a number of powerful benchmark models, where experiments are conducted on real-world data.

scholarly journals Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

2018 ◽  
Vol 10 (3) ◽  
pp. 452 ◽  
Author(s):  
Yun-Long Kong ◽  
Qingqing Huang ◽  
Chengyi Wang ◽  
Jingbo Chen ◽  
Jiansheng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Short Term Memory ◽  
Satellite Image ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Disturbance Detection

scholarly journals System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model

2018 ◽  
Vol 8 (4) ◽  
pp. 2327 ◽  
Author(s):  
Heni Kusdarwati ◽  
Samingun Handoyo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Linear Trend ◽  
Stationary Time Series ◽  
Series Data ◽  
Training Time ◽  
Stationary Time ◽  
Computation Process ◽  
Smooth Coefficients

This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.

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