We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

https://www.econstor.eu/bitstream/10419/243586/1/1666081477.pdf

Integrated Economic Adoption Model for residential grid-connected photovoltaic systems: An Australian case study

Long-term outlook for global rare earth production

The development of biorefinery processes to platform chemicals for most lignocellulosic substrates, results in side processes to intermediates such as oligosaccharides. Agrofood wastes are most amenable to produce such intermediates, in particular, cellooligo-saccharides (COS), pectooligosaccharides (POS), xylooligosaccharides (XOS) and other less abundant oligomers containing mannose, arabinose, galactose and several sugar acids. These compounds show a remarkable bioactivity as prebiotics, elicitors in plants, food complements, healthy coadyuvants in certain therapies and more. They are medium to high added-value compounds with an increasing impact in the pharmaceutical, nutraceutical, cosmetic and food industries. This review is focused on the main production processes: autohydrolysis, acid and basic catalysis and enzymatic saccharification. Autohydrolysis of food residues at 160–190 °C leads to oligomer yields in the 0.06–0.3 g/g dry solid range, while acid hydrolysis of pectin (80–120 °C) or cellulose (45–180 °C) yields up to 0.7 g/g dry polymer. Enzymatic hydrolysis at 40–50 °C of pure polysaccharides results in 0.06–0.35 g/g dry solid (DS), with values in the range 0.08–0.2 g/g DS for original food residues.

https://www.mdpi.com/2311-5637/6/1/31/pdf

Production of Oligosaccharides from Agrofood Wastes

The 4E analysis is utilized on a bulky combined cycle power plant (CCPP) with a dual pressure recovery boiler and an additional duct burner. Multi-objective evolutionary optimizations have been applied to obtain the best state of the heat recovery steam generator (HRSG), saturated temperature, cost reduction, and carbon dioxide emission, simultaneously. For the validation, the authentic data has been collected from an implemented CCPP. This comprehensive study has been performed to perceive the relation between the most significant parameters on the performance of CCPP. The main obtained results include five points. First, the thermal recovery boiler and combustion chamber have the highest exergy destruction among the power plant components. Second, the optimization based on the entire cycle at all temperatures has no economic justification, and its total exergy efficiency and cost are better than optimizations based on the recovery boiler and HRSG. Third, the value of the CCPP decision parameters is highly dependent on the ambient temperature. Therefore, it is not possible to apply the same value for CCPP at various temperatures. Fourth, the genetic algorithm improved the optimized cycle parameters by considering two objectives of the power plant costs reduction and CO2 emission. Fifth, the combined cycle with the nameplate function generates less NOx and monoxide than relative loads. Using such combined cycles with dual pressure recovery boiler and additional duct reduces the normalized Co2 emissions by 158.67 kg MWh .

Multi-Objective Evolutionary Optimization & 4E analysis of a bulky combined cycle power plant by CO2/ CO/ NOx reduction and cost controlling targets

The paper presents the results of forecasting the dynamics of the depletion of conventional energy resources, including hydrocarbons (coal, oil, natural gas) and natural uranium, which currently form the mainstay of the energy supply of the economy. A balance model developed by the authors shows the dynamics of the growing shortage of exhaustible conventional energy resources after 2040 at various energy-consumption rates. For the time being, it is unclear which nonconventional primary energy sources are able to supply advanced commercially acceptable fast-growing energy systems (including capital investment) in order to equalize the rapidly growing primary energy shortages expected in the 1940s. The most advanced energy technology with almost unlimited resources that has reached the near-commercial stage of development can be nuclear fuel breeding.

Forecasting the Dynamics of the Depletion of Conventional Energy Resources

Criteria of return on investment in nuclear energy

The economic expediency of re-enrichment of depleted uranium with the possibility of subsequent disposal of secondary waste is evaluated. Three variants of bringing depleted uranium as a raw material into the production of natural-uranium equivalent and enrichment of uranium product are examined. Methods are proposed for evaluating the expediency of producing a natural-uranium equivalent from depleted uranium under different market conditions and in different operating regimes of the separation cascade as well as methods of evaluating the value of depleted uranium in the production of a natural-uranium equivalent and the production of enriched uranium product. It is shown that an effective method of bringing depleted uranium into enrichment is diversified production of enriched uranium product and not the production of a natural-uranium equivalent.

Economic Efficiency of Bringing Depleted Uranium into Enrichment

An economic and mathematical model has been developed to determine the competitiveness of REMIX-A and -C uranium-plutonium fuel obtained by reprocessing spent fuel from thermal reactors. The presented model makes it possible to calculate the cost metrics of nuclear fuel components and the fuel component of the cost of NPP electricity as a function of the cost metrics of key process changeovers. It is shown that as the market quotations for natural uranium and separation work increase, REMIX fuel becomes more competitive and, more importantly, all the more significant, the lower the cost of reprocessing spent fuel. However, even with zero cost of reprocessing the gain from the introduction of REMIX fuel does not exceed 25%. At the same time, its use allows a 20-fold reduction in the mass of radioactive waste going to final disposal, 24% (REMIX-A) to 36% (REMIX-C) savings of natural uranium, and 8–18% of separation work (REMIX-A) as well as 18% (REMIX-A) to 40% (REMIX-C) reduction in the accumulation of depleted uranium hexafluoride.

Competitiveness Assessment of Regenerated Uranium-Plutonium Remix Fuel in Thermal Reactors

/pdf/the-value-of-spent-nuclear-fuel-as-a-raw-material-for-16anmmzwod.pdf

V. V. Kharitonov

Papers

Forecasting the Dynamics of the Depletion of Conventional Energy Resources

Criteria of return on investment in nuclear energy

Economic Efficiency of Bringing Depleted Uranium into Enrichment

Competitiveness Assessment of Regenerated Uranium-Plutonium Remix Fuel in Thermal Reactors

The value of spent nuclear fuel as a raw material for fueling reactors on thermal neutrons