Forecast of Solar Irradiance Using Chaos Optimization Neural Networks

Cao, Shukun; Weng, Wenbing; Chen, Jianbo; Liu, Weidong; Yu, Guoqing; Cao, Jinxin

doi:10.1109/appeec.2009.4918387

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…Meanwhile, G b represents the global best position achieved through the comparison of the personal bests P bi of an individual particle for each iteration. Additionally, we update the condition for G b and P bi , as illustrated in Equations (8) and (9), when the condition is true:…”

Section: Discussionmentioning

confidence: 99%

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Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

et al. 2018

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The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE.

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Section: Discussionmentioning

confidence: 99%

“…The methods for forecasting PV systems are classified into two, namely, direct and indirect approaches. In the indirect methods, the environmental parameters like solar irradiance and temperature are first predicted by using historical data [8]. Then, the output power is calculated using relevant mathematical models.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

et al. 2018

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“…These include nonlinear system identification, function learning and time series forecasting [19][20][21]. Some researchers have applied wavelet based models for analysis of irradiance time series and employ the analysis for prediction [23][24][25], used discrete wavelet transform to decompose the time series into various scales and apply feedforward neural network at each scale, as described in [22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of some physico-chemical parameters of water in Alton Reservoir, Suffolk, England

Silva

Naranjo

Rodrigues

et al. 2020

Wseas Transactions on Environment and Development

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Predict water quality variables such as Chlorophyll-a (CHL), Algae, Turbidity and Total Suspended Solids (TSS) are important for the analysis of freshwater ecosystems, that are significant not only for human populations but also essential for plant and animal diversity. However, monitoring all these variables from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms, because in high concentrations, they form scum on the water surface, which is a concern for public health due to the production of toxins. This article describes empirical algorithms to estimate these variables using LandSat-8 and Sentinel-2 images, multi-spectral instrument data, the Landsat spatial resolution (30 m) and imagery from the Sentinel-2 sensor, with a resampled 10 m spatial resolution can be used for environmental monitoring. These images, analyzed by Wavelets Neural Networks can be very useful to estimate physico-chemical and biological parameters of water. This approach is applied in Alton water reservoir, Suffolk, UK using spatial and temporal scales. The Alton Reservoir is the largest in Suffolk, with a perimeter of over 8 miles (13 km). This article presents techniques based on wavelets neural networks and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and Least Square Estimat, which are well suited to predict data sequences stemming from real-world applications techniques. The prediction behavior shows good forecasts as (NMSE = 0.00004; MARE = 0.00078, MSE =0.00013) for test data, results showed that the predicted values have good accurate. This article contributes to improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs.

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“…Among the techniques employed in solar radiation forecasting, it can be pointed out that the Auto-Regressive Integrated with Moving Average (ARIMA) (PERDOMO et al, 2010), the Artificial Neural Networks (ANN) (DENG et al, 2010;YANLING et al, 2012;YONA;SENJYU, 2009;ZERVAS, et al, 2008;ZHANG; INDEPENDENT JOURNAL OF MANAGEMENT & PRODUCTION (IJM&P) http://www.ijmp.jor.br v. 7, n. 1, January -March 2016ISSN: 2236 BEHERA, 2012), the Kalman Filter (CHAABENE; AMMAR, 2008) and the different ways of combining wavelet orthonormal basis and ANN (CAO et al, 2009;ZHOU et al, 2011;TEIXEIRA JR., et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Radiation Forecasting by Using an Iterative Combination of Wavelet Artificial Neural Networks

Royer

Wilhelm

Teixeira

et al. 2016

Ind. Jour. Man. & Prod.

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The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011). However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN) which is aimed to produce shortterm solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating Wavelet Components (WC); at second one, these WCs are individually modeled by the k different ANNs, where , and the 5 best forecasts of each WC are combined by means of another ANN,

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Forecast of Solar Irradiance Using Chaos Optimization Neural Networks

Cited by 16 publications

References 5 publications

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

Prediction of some physico-chemical parameters of water in Alton Reservoir, Suffolk, England

Short-Term Solar Radiation Forecasting by Using an Iterative Combination of Wavelet Artificial Neural Networks

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