A support vector machine–firefly algorithm-based model for global solar radiation prediction

Olatomiwa, Lanre; Mekhilef, Saad; Shamshirband, Shahaboddin; Mohammadi, Kasra; Petković, Dalibor; Ch, Sudheer

doi:10.1016/j.solener.2015.03.015

Cited by 304 publications

(85 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although attempting to model natural phenomena has a long history, the recent application of nature inspired algorithms like firefly algorithm [6], neuro fuzzy technique [7] and genetic programming [8] in the area of soft computing and also recent improvements in forecasting approaches [9,10], has lead to more accurate analysis and predictions, and thereby causing a noticeable growth of interest in this field [11][12][13][14][15][16][17][18]. However, attempts at improving signal extraction and forecasting using bio-inspired algorithms is a relatively new area of research.…”

Section: Introductionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

View full text Add to dashboard Cite

Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.

show abstract

Section: Introductionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

View full text Add to dashboard Cite

show abstract

“…The pure application of ANN in power systems is commonly used to solve forecasting problems. Forecasting of solar irradiance or PV power [100][101][102][103], wind speed [104][105][106], or load [107] are just some examples of successful applications of ANN in the energy domain. On the other Hybrid algorithm: differential evolution combined with particle swarm optimization (DEPSO).…”

Section: Artificial Neural Network (Ann) and Fuzzy Systems (Fs)mentioning

confidence: 99%

“…Table 7 presents such selection, and it is interesting to notice that the majority of the selected work is related to forecasting techniques [92,102,107,[114][115][116]. This trend is highly motivated by the necessity of a more efficient integration of renewable generation into existing electrical grids.…”

Section: Artificial Neural Network (Ann) Coupled With Fuzzy Clusterinmentioning

confidence: 99%

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

et al. 2018

View full text Add to dashboard Cite

This survey provides a comprehensive analysis on recent research related to optimization and simulation in the new paradigm of power systems, which embraces the so-called smart grid. We start by providing an overview of the recent research related to smart grid optimization. From the variety of challenges that arise in a smart grid context, we analyze with a significance importance the energy resource management problem since it is seen as one of the most complex and challenging in recent research. The survey also provides a discussion on the application of computational intelligence, with a strong emphasis on evolutionary computation techniques, to solve complex problems where traditional approaches usually fail. The last part of this survey is devoted to research on large-scale simulation towards applications in electricity markets and smart grids. The survey concludes that the study of the integration of distributed renewable generation, demand response, electric vehicles, or even aggregators in the electricity market is still very poor. Besides, adequate models and tools to address uncertainty in energy scheduling solutions are crucial to deal with new resources such as electric vehicles or renewable generation. Computational intelligence can provide a significant advantage over traditional tools to address these complex problems. In addition, supercomputers or parallelism opens a window to refine the application of these new techniques. However, such technologies and approaches still need to mature to be the preferred choice in the power systems field. In summary, this survey provides a full perspective on the evolution and complexity of power systems as well as advanced computational tools, such as computational intelligence and simulation, while motivating new research avenues to cover gaps that need to be addressed in the coming years.

show abstract

“…The Linke must be derived from one of the three methods: (1) from the ratio between measured global radiation and computed values of clear sky global radiation, (2) from other climatologic data such as cloudiness [23], or (3) directly from shortwave surface irradiance measured by satellites [24]. Although satellite-based models may be suitable for solar radiation estimation in large regions, their disadvantages are high cost and lack of historical records, because these methods are comparably new [25].…”

Section: Introductionmentioning

confidence: 99%

A novel time-effective model for daily distributed solar radiation estimates across variable terrain

Mirmasoudi

Byrne

Kroebel³

et al. 2018

Int J Energy Environ Eng

View full text Add to dashboard Cite

Accurate and precisess estimation of spatio-temporal variability of solar radiation is critical. Some commonly used models evaluate this variability using methods in which the data required for estimating atmospheric attenuation may not be easily accessible for some study areas. Here, a daily solar radiation estimation method which uses ambient air temperature, a Digital Elevation Model, time of year, and monthly radiation estimates from Solar Analyst model has been proposed. The objective was to use air temperature-based empirical models for atmospheric transmissivity and diffuse fractions to vary total monthly radiation estimation from Solar Analyst, and then calculate total daily radiation as a fraction of total monthly radiation by applying a daily transmissivity-based ratio, as air temperature data are readily available at most locations on the planet. Results revealed that daily solar radiation can be estimated very well, with Mean Absolute Bias Error of around 40-53 W m −2 or Mean Bias Error of ± 10%, under all sky conditions at seven sites in diverse climate regions, using significantly less input data. The presented method is an improvement over previously used methods with Mean Bias Error of under 10% but more input parameters. Furthermore, the hourly solar radiation values can be calculated using the presented method using the ratio between daily and hourly radiation, for example from literature values and estimated daily insolation. The result also showed that the method is more useful for those stations with substantially higher numbers of sunny days than cloudy or partly cloudy days because the uncertainty of the model decreased from cloudy to sunny sky conditions. The implemented Digital Elevation Models environment of this method makes it applicable in many studies that need spatial estimation of solar radiation, especially for solar energy generation projects. Keywords Solar radiation · Digital elevation model · Solar analyst · Atmospheric transmissivity · Air temperature Abbreviations

show abstract

A support vector machine–firefly algorithm-based model for global solar radiation prediction

Cited by 304 publications

References 57 publications

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Survey on Complex Optimization and Simulation for the New Power Systems Paradigm

A novel time-effective model for daily distributed solar radiation estimates across variable terrain

Contact Info

Product

Resources

About