A Survey on Solar Energy Prediction using AI based Techniques

Artificial Intelligence termed as the coined term AI is being used in several applications; wherein the data complexity is high of the size is non-trivially high. This paper presents a survey on AI allied techniques for solar irradiation prediction problems where the challenges mentioned for the basic AI problems to encounter have to pertain keeping in mind the size and the complexity of the data. The various ANN based structures with the relevant challenges gave been cited. The mathematical computation of the error descent for neural architectures has also been provided. It is expected that this survey would pave a path for future researchers in designing their research around the framework of ANN design

Modelling of a PV array and short time prediction of solar insolation

The thesis describes firstly PV array modeling and simulation using a two diode model. It is necessary to represent the dynamic characteristics of a PV cell through a two diode model. From the simulation studies pursued in the thesis it is envisaged that the two diode model representation of a PV array provides improved accuracy even at low solar irradiation levels. When compared with a single diode model representation, the two diode model described in the thesis gives improved representation of the PV array. In a single diode representation, the input parameters of the PV cell were approximately with seven parameters namely IPV, Io1,Io2, Rp, Rs,a1,a2 ; but in this thesis the number of inputs have been reduced to four as it has been assumed that IO1 = IO2 while the values of a1 a2are chosen arbitrarily from [5].The reason behind going for this model is that the input parameters have been reduced so as to reduce the computational time. The accuracy of the proposed two diode model is verified by applying it to a monocrystalline Kyocera PV cell obtained from the datasheet [4] described in Table1.Further the two diode model is useful to find the I-V and P-V curves in standard test condition since it is fast, simple and accurate as well as it leads to showcase P-V and I-V curves in large array simulation and in partial shading condition. Subsequently, the thesis describes an algorithm to predict solar irradiation as the solar insolation is intermittent in nature. Hence this work considers development of an artificial wavelet neural network to determine solar insolation

Solar and wind quantity 24 h-series prediction using PDE-modular models gradually developed according to spatial pattern similarity

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments.Web of Science163art. no. 108

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that interdependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

Solar Irradiance Forecasting and Implications for Domestic Electric Water Heating

As the effects of burning fossil fuels continues to present its prevalence, the interests in alternative forms of energy is expanding. Within the home, the domestic electrical water heater accounts for approximately 17% of its energy consumption. Reducing the amount of energy required to produce hot water from this thermal system alone can have a significant effect on reducing its carbon footprint. In this presented work, a modeled domestic electrical water heater was supplied photovoltaic and on-grid electrical power to increase its energy efficiency. As photovoltaic (PV) energy is directly related to solar irradiation, it is important to receive accurate solar irradiance data for the area and to forecast future solar irradiance outputs to determine optimal energy input. A Kipp & Zonen solar tracker, capable of Baseline Surface Radiation Network (BSRN) level data collection, was installed on Georgia Southern University’s campus to determine extremely accurate solar irradiance. Future irradiance data based on the historical data was then predicted by using artificial neural network (ANN) methods and those results were used to determine future PV output. The system was evaluated strictly by modeling the PV system and domestic electric water heater (DEWH), and then the PV system was integrated into the operation of the DEWH. In comparison to the typical operation of the DEWH, a PV inclusive DEWH produced a significant decrease in the on-grid energy dependency of the entire system

Long-term changes in the north–south asymmetry of solar activity : a nonlinear dynamics characterization using visibility graphs

Peer reviewedPublisher PD

Long-term changes in the north-south asymmetry of solar activity: A nonlinear dynamics characterization using visibility graphs

Solar activity is characterized by complex dynamics superimposed onto an almost periodic, approximately 11-year cycle. One of its main features is the presence of a marked, time-varying hemispheric asymmetry, the deeper reasons for which have not yet been completely uncovered. Traditionally, this asymmetry has been studied by considering amplitude and phase differences. Here, we use visibility graphs, a novel tool of nonlinear time series analysis, to obtain complementary information on hemispheric asymmetries in dynamical properties. Our analysis provides deep insights into the potential and limitations of this method, revealing a complex interplay between factors relating to statistical and dynamical properties, i.e., effects due to the probability distribution and the regularity of observed fluctuations. We demonstrate that temporal changes in the hemispheric predominance of the graph properties lag those directly associated with the total hemispheric sunspot areas. Our findings open a new dynamical perspective on studying the north-south sunspot asymmetry, which is to be further explored in future work