Short-term PV power forecasting using hybrid GASVM technique

VanDeventer, William; Jamei, Elmira; Thirunavukkarasu, Gokul Sidarth; Seyedmahmoudian, Mehdi; Soon, Tey Kok; Horan, Ben; Mekhilef, Saad; Stojcevski, Alex

doi:10.1016/j.renene.2019.02.087

Cited by 227 publications

(92 citation statements)

References 27 publications

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“…According to this comprehensive review, the hybrid models show superiority in comparison to models that only use machine learning or mathematical techniques [1]. A hybrid model employing a genetic algorithm (GA) based weight optimization for a support vector machine (SVM) is proposed for a single-step prediction in [6]. A single-step ahead forecasting algorithm combining meta-heuristic optimization and back propagation neural network (BPNN) is proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

“…The model proposed in [9] employs the attention mechanism and LSTM NN for single-step ahead forecasting of PV generation, while exploring 7.5 min to 1 hour sampling resolutions. The techniques in [6]- [9] rely mainly on the PV generation time series, which can achieve reasonable accuracy in the case of single-step ahead predictions. However, for multistep ahead predictions, reliance on the generation time series only may result in inadequate performance.…”

Section: Introductionmentioning

confidence: 99%

“…The input activation vector i(τ ) ∈ R J , the forget activation vector f (τ ) ∈ R J , and the output activation vector o(τ ) ∈ R J are updated at each time step of the input sequence by utilizing a sigmoid activation function (σ), as shown in Equations (4), (5),(7). A hyperbolic tangent activation function (φ) is used to compute intermediate cell states, named as the cell candidates c(τ ) ∈ R J , as shown in Equation(6). The corresponding (J × K) input weight matrices are concatenated as…”

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confidence: 99%

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Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

2020

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In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power generation using a long short term memory (LSTM) neural network (NN). A synthetic weather forecast is created for the targeted PV plant location by integrating the statistical knowledge of historical solar irradiance data with the publicly available type of sky forecast of the host city. To achieve this, a K-means algorithm is used to classify the historical irradiance data into dynamic type of sky groups that vary from hour to hour in the same season. In other words, the types of sky are defined for each hour uniquely using different levels of irradiance based on the hour of the day and the season. This can mitigate the performance limitations of using fixed type of sky categories by translating them into dynamic and numerical irradiance forecast using historical irradiance data. The proposed synthetic weather forecast is proved to embed the statistical features of the historical weather data, which results in a significant improvement in the forecasting accuracy. The performance of the proposed model is investigated using different intraday horizon lengths in different seasons. It is shown that using the synthetic irradiance forecast can achieve up to 33% improvement in accuracy in comparison to that when an hourly categorical type of sky forecast is used, and up to 44.6% in comparison to that when a daily type of sky forecast is used. This highlights the significance of utilizing the proposed synthetic forecast, and promote a more efficient utilization of the publicly available type of sky forecast to achieve a more reliable PV generation prediction. Moreover, the superiority of the LSTM NN with the proposed features is verified by investigating other machine learning engines, namely the recurrent neural network (RNN), the generalized regression neural network (GRNN) and the extreme learning machine (ELM). INDEX TERMS PV power forecasting, machine learning, LSTM, neural network, deep learning, synthetic weather forecast.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

2020

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“…In [83] a hybrid SVM-GA model has been devised and validated for the short-term PV power forecasting. The SVM technique was used to classify the weather, while the GA to optimize for the optimization of the model.…”

Section: Hybrid Methods-based Forecastingmentioning

confidence: 99%

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

et al. 2020

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Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic.

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“…Regarding the PV forecasting module, various methods were tested and Support Vector Regression (SVR) was selected as it produces the best estimations. As in many other studies [8,[10][11][12], this method is used to correlate the inputs, usually irradiation and temperature, with the output, which is the power production of the PV. The PV forecasting module structure is depicted in Fig.…”

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confidence: 99%

Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

et al. 2019

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Photovoltaic (PV) systems constitute one of the most promising renewable energy sources, especially for warm and sunny regions like the southern-European islands. In such isolated systems, it is important to utilize clean energy in an optimal way in order to achieve high renewable penetration. In this operational strategy, a Battery Energy Storage System (BESS) is most often used to transfer an amount of the stored renewable energy to the peak hours. This study presents an integrated energy management methodology for a PV-BESS energy system targeting to make the load curve of the conventional fuel based units as smooth as possible. The presented methodology includes prediction modules for short-term load and PV production forecasting using artificial neural, and a novel, optimized peak shaving algorithm capable of performing each day’s maximum amount of peak shaving and smoothing level simultaneously. The algorithm is coupled with the overall system model in the Modelica environment, on the basis of which dynamic simulations are performed. The simulation results are compared with the previous version of the algorithm that had been developed in CERTH, and it is revealed that the system’s performance is drastically improved. The overall approach proves that in such islanding systems, a PV-BESS is a suitable option to flatten the load of the conventional fuel based units, achieve steadier operation and increase the share of renewable energy penetration to the grid.

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Short-term PV power forecasting using hybrid GASVM technique

Cited by 227 publications

References 27 publications

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Advanced energy management system based on PV and load forecasting for load smoothing and optimized peak shaving of islanded power systems

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