scholarly journals Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 721 ◽  
Author(s):  
Songpu Ai ◽  
Antorweep Chakravorty ◽  
Chunming Rong
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Low Voltage ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Power Demand ◽  
Network Configuration ◽  
Demand Prediction ◽  
Home Energy Management ◽  
Ensemble Neural Network

The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors.

Evaluating ARIMA-Neural Network Hybrid Model Performance in Forecasting Stationary Timeseries

2013 ◽  
Vol 845 ◽  
pp. 510-515
Author(s):  
Seyed Navid Seyedi ◽  
Pouyan Rezvan ◽  
Saeed Akbarnatajbisheh ◽  
Syed Ahmad Helmi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Model Performance ◽  
Stationary Time Series ◽  
Forecasting Model ◽  
Dynamic Nature ◽  
Demand Prediction ◽  
Forecasting Models ◽  
Artificial Neural Network Ann

Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.

scholarly journals Application of a degree-day snow depth model to a Swiss glacierised catchment to improve neural network discharge forecasts

2005 ◽  
Vol 36 (2) ◽  
pp. 99-111 ◽  
Author(s):  
G. Schumann ◽  
G. Lauener
Keyword(s):  
Neural Network ◽  
Snow Depth ◽  
Moving Average ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Low Frequency ◽  
Weather Conditions ◽  
Efficiency Criterion ◽  
Degree Day ◽  
Model Training

A trained soft artificial neural network (SANN) model was applied to the Gornera catchment (Valais Alps, Switzerland) over the melt season May to September 2001 to predict hourly discharge up to five days ahead A SANN discharge forecast for three days ahead has previously been performed on this catchment using only past discharge and past and forecast air temperature as model training inputs. In this study, present zonal snow depth was included as a model input, which was predicted for five altitudinal catchment zones using an empirical degree-day model. Hourly discharge values for up to five days ahead were reconstructed using SANN predicted daily discharge parameters along with a normalised long-term moving average model (MAHM). The efficiency criterion R2 gives a model performance of 0.927 for a 24-hour-ahead forecast and 0.824 for a 120-hour-ahead forecast. Compared to previous work, adding the snow model to the SANN model inputs considerably increases the forecast accuracy, in particular during days of progressive discharge increase and thunderstorms. The SANN model yields excellent results on days marked by stable weather conditions, with an R2 value between 0.913 and 0.995. However, the model is unable to reliably predict low frequency, high magnitude events, e.g. release of stored water from a glacial lake.

PREDICTING GLOBAL INTERNET GROWTH USING AUGMENTED DIFFUSION, FUZZY REGRESSION AND NEURAL NETWORK MODELS

2006 ◽  
Vol 05 (01) ◽  
pp. 155-171 ◽  
Author(s):  
KALLOL BAGCHI ◽  
SOMNATH MUKHOPADHYAY
Keyword(s):  
Neural Network ◽  
Diffusion Model ◽  
New Technology ◽  
Model Performance ◽  
Forecast Accuracy ◽  
Economic Indicator ◽  
Fuzzy Regression ◽  
The Internet ◽  
Neural Network Models ◽  
Global Business

Quantitative models explaining and forecasting the growth of new technology like the Internet in global business operation appear infrequently in the literature. This paper introduces two artificial intelligence (AI) models such as the neural network and fuzzy regression along with an augmented diffusion model to study and predict the Internet growth in several OECD nations. First, a linear version of an augmented diffusion model is designed. An augmented diffusion model is constructed by including an economic indicator, gross domestic product per capita, into the model. In the next step, two soft AI models are calibrated from the augmented diffusion model. Performance measures of predictions from these models on new samples show that these soft models provide improved forecast accuracy over the augmented diffusion model. The results confirm the major contribution of this research in predicting global Internet growth.

A Novel Forecast Method for Air-Conditioning Load of Public Building Considering Accumulated Temperature Effect

2015 ◽  
Vol 9 (1) ◽  
pp. 363-367
Author(s):  
Qingshan Xu ◽  
Xufang Wang ◽  
Chenxing Yang ◽  
Hong Zhu ◽  
Qingguo Yan
Keyword(s):  
Neural Network ◽  
Temperature Effect ◽  
Air Conditioning ◽  
Forecast Accuracy ◽  
Power Network ◽  
Forecast Method ◽  
Elman Neural Network ◽  
Public Building ◽  
Effective Accumulated Temperature ◽  
Accumulated Temperature

It has great significance to estimate the schedulable capacity of air-conditioning load of public building for participating the power network regulation by forecasting the air-conditioning load accurately. A novel forecast method considering the accumulated temperature effect is proposed in this paper based on Elman neural network. Firstly, the starting and ending date for forecast considering the accumulated temperature effect are determined by providing the five day sliding average thermometer algorithm which is usually adopted in aerology research. Then, the effective accumulated temperature of each day is calculated. Finally, take the effective accumulated temperature, temperature and humidity into consideration, the air-conditioning load of public building in the forecast day is acquired by Elman neural network. Simulated results show that the higher forecast accuracy can be achieved by considering the accumulated temperature effect.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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