A Genetic Programming Approach to Forecast Daily Electricity Demand

Mehr, Ali Danandeh; Bagheri, Farzaneh; Reşatoğlu, Rifat

doi:10.1007/978-3-030-04164-9_41

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…In particular, GP has been in the energy sector. For instance, Karabulut et al used GP to forecast long-term electrical power consumption in southeast Turkey, finding promising results with a low error rate [29]. Additionally, the same group used GP to forecast long term energy consumption in Turkey [30].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

et al. 2020

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Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics.

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Section: Introductionmentioning

confidence: 99%

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

et al. 2020

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Comparing machine learning approaches for estimating soil saturated hydraulic conductivity

Moosavi,

Nematollahi,

Omidifard

2024

PLoS ONE

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Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intensive, pedotransfer functions (PTFs) that rely on statistical predictors are usually integrated with the existing measurements to predict Kfs in other areas of the field. In this study some of the most appropriate machine learning approaches, including variants of artificial neural networks (ANNs) were used for predicting Kfs by some easily measurable soil attributes. The analyses were performed using 100 measurements in Bajgah Agricultural Experimental Station. First, physico-chemical inputs as bulk density (BD), initial water content (Wi), saturated water content (Ws), mean weight diameter (MWD), and geometric mean diameter (GMD) of aggregates, pH, electrical conductivity (EC), and calcium carbonate equivalent (CCE) were measured. Then, radial basis functions (RBFNNs), multilayer perceptron (MLPNNs), hybrid genetic algorithm (GA-NNs), and particle swarm optimization (PSO-NNs) neural networks were utilized to develop PTFs and compared their accuracy with the traditional regression model (MLR) using statistical indices. The statistical assessment indicated that PSO-NNs with the lowest RMSE and MAPE as well as the highest correlation coefficient (R) value provided the most accurate and robust prediction of Kfs. The prediction models ranked as PSO-NNs (R = 0.958; RMSE = 0.343; MAPE = 9.47), GA-NNs (R = 0.949; RMSE = 0.404; MAPE = 11.83), MLPNNs (R = 0.933; RMSE = 0.426; MAPE = 12.13), RBFNNs (R = 0.926; RMSE = 0.452; MAPE = 14.30), and MLR (R = 0.675; RMSE = 0.685; MAPE = 22.54) in terms of their performances for the test data set. Results revealed that all NN models particularly PSO-NNs were efficient in prediction of Kfs. However, further evaluations may be recommended for other soil conditions and input variables to quantify their potential uncertainties and wider potential and versatility before they are used in other geographical locations/soil conditions.

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Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree

Mehr

Bagheri

Safari

2020

Gazi University Journal of Science

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Several recent studies have used various data mining techniques to obtain accurate electrical energy demand forecasts in power supply systems. This paper, for the first time, compares the efficiency of the decision tree (DT) and classic genetic programming (GP) data mining models developed for electrical energy demand forecasting in Nicosia, Northern Cyprus. The models were trained and tested using daily electricity consumptions measured during the period 2011-2016 and were compared in terms of three statistical performance indices including coefficient of determination, mean absolute percentage error and concordance coefficient. The prediction results showed that the proposed models can be effectively applied to forecasts of electrical energy demand. The results also indicated that the GP is slightly superior to DT in terms of the performance indices.

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A Genetic Programming Approach to Forecast Daily Electricity Demand

Cited by 3 publications

References 19 publications

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity

Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree

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