Solar photovoltaic generation forecasting methods: A review

Modeling of solar energy systems using artificial neural network: A comprehensive review

https://escholarship.org/content/qt5dk0b8t5/qt5dk0b8t5.pdf?t=q9piey

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

An overview of numerical and mathematical modelling-based distributed generation (DG) system optimisation techniques is presented in this review paper. The objective is to compare different aspects of these two broad classes of DG optimisation techniques, explore their applications, and identify potential research directions from reviewed studies. Introductory descriptions of general electrical power system and DG system are first provided, followed by reviews on renewable resource assessment, load demand analysis, model formulation, and optimisation techniques. In renewable resource assessment model review, uncertain solar and wind energy resources are emphasised whereas applications of forecasting models have been highlighted based on their prediction horizons, computational power requirement, and training data intensity. For DG optimisation framework, (solar, wind and tidal) power generator, energy storage and energy balance models are discussed; in optimisation technique section, both numerical and mathematical modelling optimisation methods are reviewed, analysed and criticised with recommendations for their improvements. In overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired samples are generally random in nature. Thus, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the underlying sparse vectors. Provided this condition is fulfilled, we show that CS samples will also preserve the envelope of the actual signal. Exploiting this envelope preserving property of CS samples, we propose a new fast method which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals. These prototype signals are orthogonal intrinsic mode functions (IMFs) extracted from CS samples using empirical mode decomposition (EMD), which is one of the popular methods to capture the envelope of a signal. The extracted IMFs are used to seed the dictionary without even comprehending the original signal or the sensing matrix. Moreover, one can update the dictionary on-line as new CS samples are available. In particularly, to recover first L signals (∊ Rn) at the decoder, one can seed the dictionary in just O(nL log n) operations, that is far less as compared to existing approaches. The efficiency of the proposed approach is demonstrated experimentally for recovery of speech signals.

Making sense of randomness: Fast signal recovery from compressive samples

ABSTRACT In this work, a band selection technique based on FDA and functional PCA is proposed. The method selects shape-preserving, discriminative bands which can highlight the important characteristics, variations as well as patterns of the hyperspectral data such that the differences between data from different classes become more apparent. The selection is performed in two stages. The first stage is selection of shape-preserving, keypoint bands which explain the trend characteristics of the reflectance curves in the hyperspectral data. These bands are able to preserve the functional representation of the curves as close as possible to their original representation using all the bands. The second stage involves selection of bands with good discriminative capabilities from the previously selected keypoint bands. For this, a novel iterative band selection technique using supervised functional principal component analysis (SFPCA) is proposed. The proposed band selection algorithm is the first approach reported in literature to explore the functional behaviour of HSI. An objective comparison of the proposed approach with several widely used, state-of-art band selection methods demonstrate a significant improvement in the classification accuracy.

Selection of shape-preserving, discriminative bands using supervised functional principal component analysis

Detection of mitotic HEp-2 cell images: role of feature representation and classification framework under class skew

Estimation of neuronal task information in fMRI using zero frequency resonator

In compressed sensing (CS) framework, a signal is sampled below Nyquist rate, and the acquired compressed samples are generally random in nature. However, for efficient estimation of the actual signal, the sensing matrix must preserve the relative distances among the acquired compressed samples. Provided this condition is fulfilled, we show that CS samples will preserve the envelope of the actual signal even at different compression ratios. Exploiting this envelope preserving property of CS samples, we propose a new fast dictionary learning (DL) algorithm which is able to extract prototype signals from compressive samples for efficient sparse representation and recovery of signals. These prototype signals are orthogonal intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD), which is one of the popular methods to capture the envelope of a signal. The extracted IMFs are used to build the dictionary without even comprehending the original signal or the sensing matrix. Moreover, one can build the dictionary on-line as new CS samples are available. In particularly, to recover first $L$ signals ($\in\mathbb{R}^n$) at the decoder, one can build the dictionary in just $\mathcal{O}(nL\log n)$ operations, that is far less as compared to existing approaches. The efficiency of the proposed approach is demonstrated experimentally for recovery of speech signals.

Anil Kumar Sao

Papers

Making sense of randomness: Fast signal recovery from compressive samples

Selection of shape-preserving, discriminative bands using supervised functional principal component analysis

Detection of mitotic HEp-2 cell images: role of feature representation and classification framework under class skew

Estimation of neuronal task information in fMRI using zero frequency resonator

Making sense of randomness: an approach for fast recovery of compressively sensed signals.