Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Chen, Serena H.; Jakeman, Anthony J.; Norton, J.P.

doi:10.1016/j.matcom.2008.01.028

Cited by 253 publications

(121 citation statements)

References 158 publications

(178 reference statements)

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“…However, to choose both the most suitable learning algorithm and the proper size of the training set which minimizes the error is a challenge which should be faced in each case study [29][30][31].…”

Section: Training Database Composition Approachesmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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Abstract:The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network's main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.

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Section: Training Database Composition Approachesmentioning

confidence: 99%

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

et al. 2018

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“…The errors introduced by ANNs and physical methods sometimes are already too high for electricity market and RES imbalance issues. This paper presents a new auxiliary hybrid system [16] model that combines a soft-computing model based on ANN and physical models of the total global irradiance (the clear sky curve) for medium-term power of a PV plant. The results are compared to those ones coming from a statistical ANN method.…”

Section: Open Accessmentioning

confidence: 99%

“…According to their different groupings they can be: sequential, auxiliary and embedded. Our new proposed hybrid system is an auxiliary one as the first paradigm passes its output to the second in order to generate the output [16].…”

Section: Energy Forecast Modelsmentioning

confidence: 99%

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

et al. 2015

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Abstract:The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.

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“…Briefly, input data are translated by the fuzzy-logic model into a consistent language, processed according to rules and fuzzy membership functions, then the results from the rule-based assessment are translated into output values through a final defuzzification step ( Fig. 1) (Chen et al 2008). …”

Section: Data Collectionmentioning

confidence: 99%

Assessment of nitrate leakage and N2O emission from five environmental-friendly agricultural practices using fuzzy logic method and empirical formula

Qin

Wang

et al. 2015

Environ Monit Assess

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Agricultural nonpoint source pollution in China has been the major environmental problem, so environmental-friendly agricultural practices (EAPs) must be promoted to improve environmental quality. However, the most suitable practices for each agricultural region must first be identified. Thus, in the presented study a fuzzy-logic method and a revised empirical formula were used to assess nitrate leakage and N 2 O emissions, respectively, and to compare five EAPs in Xinxiang, a major grain-producing county in Henan Province, China. The required information was collected in face-to-face interviews with 10 extension service experts from the county, using a questionnaire to explore their opinions of the EAPs currently adopted by smallholder farmers, as well as the amounts, frequencies, varieties and proportions of nitrogen fertilizers applied annually. The results indicate that reduced tillage, soil testing and fertilizer recommendations would be the most appropriate practices to initially promote on a large scale in Xinxiang.

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Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Cited by 253 publications

References 158 publications

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Assessment of nitrate leakage and N2O emission from five environmental-friendly agricultural practices using fuzzy logic method and empirical formula

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