scholarly journals Electrical Load Demand Forecasting Using Feed-Forward Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7644
Author(s):  
Eduardo Machado ◽  
Tiago Pinto ◽  
Vanessa Guedes ◽  
Hugo Morais
Keyword(s):  
Machine Learning ◽  
Renewable Energy Sources ◽  
Demand Forecasting ◽  
Electricity Sector ◽  
Data Availability ◽  
Forecast Errors ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Load Demand ◽  
Demand Forecasts

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.

scholarly journals Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

2021 ◽  
Vol 11 (15) ◽  
pp. 6787
Author(s):  
Jože M. Rožanec ◽  
Blaž Kažič ◽  
Maja Škrjanc ◽  
Blaž Fortuna ◽  
Dunja Mladenić
Keyword(s):  
Machine Learning ◽  
Automotive Industry ◽  
Demand Management ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Forecast Errors ◽  
Manufacturing Companies ◽  
Case Scenario ◽  
Crucial Component

Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

scholarly journals Estimasi Pada Effort Perangkat Lunak dengan Pendekatan Feed Forward Neural Network Backpropagation (FFNN-BP)

2021 ◽  
Vol 12 (2) ◽  
pp. 89
Author(s):  
As'ary Ramadhan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Relative Error ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Architecture Model ◽  
Cocomo Ii

Estimasi biaya pengembangan proyek perangkat lunak merupakan salah satu masalah yang kritis dalam rekayasa perangkat lunak. Kegagalan dari proyek perangkat lunak diakibatkan ketidak akuratannya estimasi sumber daya yang dibutuhkan. Beberapa model telah dikembangkan dalam beberapa puluh tahun belakangan ini. Untuk meberikan keakuratan dalam estimasi biaya proyek perangkat lunak masih menjadi tantangan hingga saat ini. Tujuan dilakukannya penelitian ini meningkatkan akurasi estimasi biaya proyek perangkat lunak dengan menerapkan algoritma genetika sebagai proses pelatihan pada Feed Forward Neural Network Backpropagation (FFNN-BP) yang mengakomodasi formula dari Post Architecture Model (COCOMO II). Magnitude of Relative Error (MRE) dan Mean Magnitude of Relative-Error (MMRE) digunakan sebagai pengkuran indikasi kinerja. Hasil percobaan menunjukkan bahwa model yang diusulkan memberikan hasil estimasi biaya proyek perangkat lunak menjadi lebih akurat dari COCOMO II dan FFNN-BP. Dalam kasus ini MMRE untuk COCOMO II adalah 74.68%, FFNN-BP adalah 39.90% .  Kata kunci: COCOMO II, Machine Learning, Proyek Manajemen IT, Backpropagation

scholarly journals The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 4073
Author(s):  
Belete Biazen Bezabeh ◽  
Abrham Debasu Mengistu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Performance Analysis ◽  
Transfer Function ◽  
Soil Classification ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Feature Descriptor ◽  
Feed Forward Neural Network ◽  
Feed Forward

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.

scholarly journals A Survey and Analysis of Extreme Machine Learning Models and Its Techniques

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Kumar Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

scholarly journals An Analysis of Extreme Machine Learning Models

2021 ◽  
Author(s):  
Shubhangi Pande ◽  
Neeraj Rathore ◽  
Anuradha Purohit
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Global Optimum ◽  
Least Square ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Feed Forward

Abstract Machine learning applications employ FFNN (Feed Forward Neural Network) in their discipline enormously. But, it has been observed that the FFNN requisite speed is not up the mark. The fundamental causes of this problem are: 1) for training neural networks, slow gradient descent methods are broadly used and 2) for such methods, there is a need for iteratively tuning hidden layer parameters including biases and weights. To resolve these problems, a new emanant machine learning algorithm, which is a substitution of the feed-forward neural network, entitled as Extreme Learning Machine (ELM) introduced in this paper. ELM also come up with a general learning scheme for the immense diversity of different networks (SLFNs and multilayer networks). According to ELM originators, the learning capacity of networks trained using backpropagation is a thousand times slower than the networks trained using ELM, along with this, ELM models exhibit good generalization performance. ELM is more efficient in contradiction of Least Square Support Vector Machine (LS-SVM), Support Vector Machine (SVM), and rest of the precocious approaches. ELM’s eccentric outline has three main targets: 1) high learning accuracy 2) less human intervention 3) fast learning speed. ELM consider as a greater capacity to achieve global optimum. The distribution of application of ELM incorporates: feature learning, clustering, regression, compression, and classification. With this paper, our goal is to familiarize various ELM variants, their applications, ELM strengths, ELM researches and comparison with other learning algorithms, and many more concepts related to ELM.

Machine learning and optimization-based modeling for asset management: a case study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrés Muñoz-Villamizar ◽  
Carlos Yohan Rafavy ◽  
Justin Casey
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Asset Management ◽  
Optimization Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Content Type ◽  
Demand Prediction

PurposeThis research is inspired by a real case study from a pump rental business company across the US. The company was looking to increase the utilization of its rental assets while, at the same time, keeping the cost of fleet mobilization as efficient as possible. However, decisions for asset movement between branches were largely arranged between individual branch managers on an as-needed basis.Design/methodology/approachThe authors propose an improvement for the company's asset management practice by modeling an integrated decision tool which involves evaluation of several machine learning algorithms for demand prediction and mathematical optimization for a centrally-planned asset allocation.FindingsThe authors found that a feed-forward neural network (FNN) model with single hidden layer is the best performing predictor for the company's intermittent product demand and the optimization model is proven to prescribe the most efficient asset allocation given the demand prediction from FNN model.Practical implicationsThe implementation of this new tool will close the gap between the company's current and desired future level of operational performance and consequently increase its competitivenessOriginality/valueThe results show a superior prediction performance by a feed-forward neural network model and an efficient allocation decision prescribed by the optimization model.

scholarly journals Using A Feed Forward Neural Network Algorithm to Predict Prices of Multiple Cryptocurrencies

2021 ◽  
Vol 6 (5) ◽  
pp. 15-19
Author(s):  
Sina E. Charandabi ◽  
Kamyar Kamyar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Learning Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Price Data ◽  
Machine Learning Model ◽  
Feed Forward Neural Networks ◽  
Neural Network Algorithm

This paper initially presents a nontechnical overview of cryptocurrency, its history, and the technicalities of its usage as a means of exchange. Bitcoin’s working methodology and mathematical baseline is further presented in more depth. For the remaining majority of the paper, recent cryptocurrency price data of Bitcoin, Ethereum, Tether, Dogecoin, and Binance coin was used to train a machine learning model of Feed Forward Neural Networks to predict future prices for each of the datasets. Further and in conclusion, the results are discussed, and the efficiency and accuracy of these models are evaluated.

Improving the Model for Energy Consumption Load Demand Forecasting

2012 ◽  
Vol 132 (3) ◽  
pp. 235-243
Author(s):  
Pituk Bunnoon ◽  
Kusumal Chalermyanont ◽  
Chusak Limsakul
Keyword(s):  
Energy Consumption ◽  
Demand Forecasting ◽  
Load Demand
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