Best Architecture Recommendations of ANN Backpropagation Based on Combination of Learning Rate, Momentum, and Number of Hidden Layers

This article discusses the results of research on the combination of learning rate values, momentum, and the number of neurons in the hidden layer of the ANN Backpropagation (ANN-BP) architecture using meta-analysis. This study aims to find out the most recommended values at each learning rate and momentum interval, namely [0.1], as well as the number of neurons in the hidden layer used during the data training process. We conducted a meta-analysis of the use of learning rate, momentum, and number of neurons in the hidden layer of ANN-BP. The eligibility data criteria of 63 data include a learning rate of 44 complete data, the momentum of 30 complete data, and the number of neurons in the hidden layer of 45 complete data. The results of the data analysis showed that the learning rate value was recommended at intervals of 0.1-0.2 with a RE model value of 0.938 (very high), the momentum at intervals of 0.7-0.9 with RE model values of 0.925 (very high), and the number of neurons in the input layer that was smaller than the number of neurons in the hidden layer with a RE model value of 0.932 (very high). This recommendation is obtained from the results of data analysis using JASP by looking at the effect size of the accuracy level of research sample data

Fast ensemble empirical mode decomposition model for forecasting crude oil and condensates

Crude oil and condensates supply and demand strives to be main authority of the sustenance of almost all country’s economy. The sudden rise in the oil price has forced the government to forecast the supply and demand of crude oil and condensates in order to make sure that the amount of crude oil meets the supply and demand of the country. Accurate forecasts can save cost, foresee scarcity of demand, and help in budgeting profit. In addition, predicting crude oil and condensate data is frequently proven to be a demanding task considering the various intricacies of oil data pattern. The main objective of this study was to forecast crude oil and condensates demand data in Malaysia using Fast Ensemble Empirical Mode Decomposition (FEEMD) model. The forecasting process using FEEMD model was performed in order to achieve the most desirable forecast accuracy of the crude oil and condensates data. The FEEMD model is an extension of the Empirical Mode Decomposition (EMD) model whereby white noise signal was added to the existing signal in the sifting process. The effectiveness of the proposed forecasting method was compared to other traditional models of ARIMA, ARIMAX and GARCH. The results revealed that the proposed FEEMD method for forecasting crude oil and condensates data was very promising as it achieved good forecast accuracy

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Combining Machine Learning and Empirical Engineering Methods Towards Improving Oil Production Forecasting

Current methods of production forecasting such as decline curve analysis (DCA) or numerical simulation require years of historical production data, and their accuracy is limited by the choice of model parameters. Unconventional resources have proven challenging to apply traditional methods of production forecasting because they lack long production histories and have extremely variable model parameters. This research proposes a data-driven alternative to reservoir simulation and production forecasting techniques. We create a proxy-well model for predicting cumulative oil production by selecting statistically significant well completion parameters and reservoir information as independent predictor variables in regression-based models. Then, principal component analysis (PCA) is applied to extract key features of a well’s time-rate production profile and is used to estimate cumulative oil production. The efficacy of models is examined on field data of over 400 wells in the Eagle Ford Shale in South Texas, supplied from an industry database. The results of this study can be used to help oil and gas companies determine the estimated ultimate recovery (EUR) of a well and in turn inform financial and operational decisions based on available production and well completion data

Forecasting Natural Gas Prices in the United States Using Artificial Neural Networks

Prediction of the natural gas price is imperative to producers, suppliers, traders, market makers, and bankers involved in the natural gas exploration, production transportation, and trading. Additionally, consumers are also highly affected by the changes in the price of oil and gas products. Several attempts have been made to model the energy commodity prices over the past few decades. Stochastic differential equation, linear and nonlinear regression, auto regression, and neural networks are the main techniques that have been implemented. In this thesis, three different categories of models are examined which are, stochastic differential equations, ARIMA, and autoregressive neural networks. The results indicate that, the NAR neural network provides a better fit to the given data as compared to the other proposed models. The three-layer NAR model with 6 hidden neurons was found to have the best performance in terms of one month ahead price prediction. The accuracy of the NARX model with 6 neurons was found to be higher than that of the other models. Although, this model provides a reasonable fit to the given data, it fails to capture the price spikes effectively. The sensitivity analysis shows that CDD/HDD temperatures, extreme minimum temperature, and WTI oil prices have an insignificant effect on the results. On the other hand, total consumption, total production, and mean temperature of weather impact the results significantly