A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm

Behera, Manoja Kumar; Nayak, Niranjan

doi:10.1016/j.jestch.2019.03.006

Cited by 66 publications

(27 citation statements)

References 35 publications

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“…The ELM algorithm offers a very fast training process with minimal tuning requirements (number of neurons in the hidden layer). In recent years, the ELM algorithm has been successfully applied for forecasting purposes in the electricity field, with approaches for short-term load forecasting [44][45][46], generation forecasting in power plants based on renewable sources [47][48][49][50] and price forecasting in electricity spot markets [30,51,52].…”

Section: Proposed Mid-term Forecasting Model Of Average Spot Pricesmentioning

confidence: 99%

Predictive Trading Strategy for Physical Electricity Futures

2020

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This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.

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Section: Proposed Mid-term Forecasting Model Of Average Spot Pricesmentioning

confidence: 99%

Predictive Trading Strategy for Physical Electricity Futures

2020

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“…To overcome this problem, a robust learner should be used to reduce the impact of noisy data on the performance of the learner. In some studies such as [9], methods have been adopted to eliminate noise.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

et al. 2020

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The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.

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“…Results showed that ELM has better performance than classical BP-ANN and performance can be further improved using PSO methods. Similar work (Behera and Nayak, 2019) proposed 3-stage model based on Empirical Mode Decomposition (EMD), Sine Cosine Algorithm (SCA), and ELM techniques. The model used measured solar radiation, ambient temperature and PV power production as inputs, with a prediction horizon of 15, 30, and 60 min.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

et al. 2019

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The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kW ac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2) performance metrics, respectively, compared to the Persistence prediction model of literature.

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A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm

Cited by 66 publications

References 35 publications

Predictive Trading Strategy for Physical Electricity Futures

Predictive Trading Strategy for Physical Electricity Futures

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

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