Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

2021 ◽  
Author(s):  
Hugo Abreu Mendes ◽  
João Fausto Lorenzato Oliveira ◽  
Paulo Salgado Gomes Mattos Neto ◽  
Alex Coutinho Pereira ◽  
Eduardo Boudoux Jatoba ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Time Series ◽  
Solar Radiation ◽  
Statistical Models ◽  
Clean Energy ◽  
Machine Learning Techniques ◽  
Exogenous Variables ◽  
Learning Techniques ◽  
Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

Solar Radiation Prediction Using Machine Learning Techniques: A Review

2019 ◽  
Vol 17 (04) ◽  
pp. 684-697 ◽  
Author(s):  
Edgar Dario Obando ◽  
Sandra Ximena Carvajal ◽  
Jairo Pineda Agudelo
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A benchmarking of machine learning techniques for solar radiation forecasting in an insular context

Solar Energy ◽  
2015 ◽  
Vol 112 ◽  
pp. 446-457 ◽  
Author(s):  
Philippe Lauret ◽  
Cyril Voyant ◽  
Ted Soubdhan ◽  
Mathieu David ◽  
Philippe Poggi
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

2020 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Observation Data ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

scholarly journals Modeling the Temporal Population Distribution of Ae. aegypti Mosquito using Big Earth Observation Data.

2020 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Observation Data ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

scholarly journals Forecasting of Air Passengers using ARIMA Modeling

2019 ◽  
Vol 8 (11S) ◽  
pp. 1050-1054
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Autocorrelation Function ◽  
Arima Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Partial Autocorrelation Function ◽  
Time Series Method ◽  
Arima Modeling

Air passengers prediction is said to be the centre of gravity of the growth. With people on the move constantly, there is bound to be some dissatisfaction amongst the customers which could be due to various reason, varying from overbooking of flights to ground operations. This dissatisfaction can be controlled till a limit, in ballpark figuring. In the past, this has been done using various machine learning techniques. For this prediction, in this project, ARIMA Modeling is used which is a time series forecasting method, based on machine learning. To test the stationarity of the data, which is done using Dickey Fuller test. If the data is stationary, it is fit into the ARIMA Model. If the data isn’t stationary, it is made stationary by differencing or by logarithmic transformation. The logarithmic method to make the data stationary. Once the data is stationary, using the Partial autocorrelation function and the autocorrelation function, values of p and q are found, which are required in the time series method. These values are then fit into the ARIMA Modeling and hence, the results are predicted. Upon the use and fitting of various models, the ARIMA(2,1,2) has been the best fit, having the least RMS and RMSE values.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

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