SEARCH · Search NASA

Results for “Solar Irradiance”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4 μm is 57% of that for the particles with the diameter of 202.8 μm. The temperatures of these two particle components increase from the initial ambient values by factors of 2 and 1.2, respectively, during the simulation time. In conclusion, the model developed allows for increased fidelity of particle–gas flow simulations with significant radiative effects.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A New Approach to Indoor Characterization of PV Module Energy Yield Parameters

Temperature and solar irradiance are among the most relevant parameters that affect the energy yield of photovoltaic cells and modules. A rise in the module temperature leads to a significant decrease in the open circuit voltage and a small increase in the short circuit current. Here we use a new tool to determine temperature coefficients as well as to assess power rating of multi c-Si, mono c-Si, CdTe, and CIGS-based PV modules in controlled indoor conditions per IEC 60891 and IEC 61853-1. We use the tool to explore how nonuniformities in module temperature affect the accuracy of the temperature coefficients.

14 SOLAR ENERGY↗

DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗

DOE OSTI2026

Comprehensive assessment of metrology techniques for heliostat efficiency and performance evaluation

Concentrating solar power plants, specifically central receiver type systems and their heliostat field, are struggling with negative reputation in the USA, due to perceived underperformance and reliability issues. This is in part due to a lack of standards for performance assessment as well as overly simplified techno-economical models. A better understanding of influences and losses along the solar radiation path from the sun, across the solar collector to the receiver, increases the fidelity of heliostat efficiency assessment as well as solar field performance predictions. Such data are currently scarce and require a complete set of metrology capabilities to evaluate direct solar irradiance, sun shape, atmospheric attenuation, reflectance, collector shape, slope errors and total beam dispersion. In preparation for establishing a 3rd party metrology platform in collaboration with Sandia National Labs, NLR conducted a scoping study on available metrology. We present an extensive overview of techniques and commercial systems for each category. Our work includes an analysis to increase understanding of strengths and limitations of the many techniques used for surface shape and slope measurement. This applies to a controlled, indoor or outdoor laboratory environment assessing a single heliostat.

14 SOLAR ENERGY↗

DOE OSTI2022

Monolayer Molecular Functionalization Enabled by Acid–Base Interaction for High-Performance Photochemical CO 2 Reduction

We report the development of a hybrid catalyst consisting of carbon nitride (CN x ) and cobalt phthalocyanine tetracarboxylic acid (CoPc-COOH), which converts CO 2 to CO with high reaction rate (1067 μmol/g·h) and high selectivity (over 98%), under simulated solar irradiation. The carboxylic acid substituents on the phthalocyanine ligands play a critical role as they bind to the amine groups of CN x to enable nearly ideal monolayer coverage of the molecular co-catalyst on the semiconductor surface and promote catalytic activity from the molecular complex. Specifically, the CN x /CoPc-COOH hybrid material achieves a reaction rate 16 times higher than a CN x material containing unsubstituted CoPc molecules. We further show that activation and deactivation of the CN x /CoPc-COOH composite, which are associated with the reduction and decomposition of CoPc-COOH, respectively, both proceed at a nearly constant rate regardless of the CO 2 reduction reaction rate. Here, the decoupling of charge carrier injection and CO 2 reduction catalysis has important mechanistic implications for future performance optimization and materials design of photocatalysts for CO 2 reduction.

14 SOLAR ENERGY↗

DOE OSTI2025

Global techno-economic and life cycle greenhouse gas emissions assessment of solar and wind based renewable hydrogen production

This study conducts a global assessment of renewable hydrogen production pathways, focusing on techno-economic performance and life cycle greenhouse gas (GHG) emissions. It evaluates standalone solar photovoltaic (PV), wind, and hybrid PV/wind systems, integrated with proton exchange membrane (PEM) electrolyzers, through multi-objective optimization and considering embodied emissions in manufacturing PV, wind and electrolyzers. Results identify optimal configurations to minimize levelized cost of hydrogen (LCOH) and carbon intensity (CI) of hydrogen, showing potential reductions of cost and CI by 2030. Standalone PV systems can achieve LCOH values smaller than 6.5 USD/kg H 2 and CI less than 2.5 kg CO 2 eq/kg H 2 in regions with high solar irradiance, such as North Africa, the Middle East and Chile. Wind systems in regions such as Middle East, North Africa, Australia and Central United States achieve LCOH below 5 USD/kg H 2 and CI under 1.5 kg CO 2 eq/kg H 2 . Hybrid systems emerge as the optimal solution for minimizing both the LCOH and CI by maximizing the use of renewable energy. Moreover, the results also indicate that, with the technological advancements, future reduction in the capital cost of renewable energy systems and the PEM electrolyzer as well as the trade of coproduct O 2 could drive the LCOH of all the RES-based hydrogen systems below 1 USD/kg H 2 and the CI below zero in different regions as Middle East, North Africa and Central United State

08 HYDROGEN↗

DOE OSTI2022

Design of a Compact Dry Cooler With an Aluminum Heat Exchanger Core for a Supercritical CO2 Power Cycle Is Evaluated for a Concentrating Solar Power Application

Abstract As the supercritical CO2 power cycle develops and the component technologies mature, there is still a need to reduce the associated capital and operating costs to maintain a competitive levelized cost of electricity (LCOE) in order to enter the market. When considering concentrating solar power (CSP) coupled with an sCO2 power block and sensible thermal storage, the technology presents a clean source for utility-scale power generation to support baseload or peak-load electrical demand. However, the LCOE of the technology is still considered higher than the competing technologies and should be reduced to better compete in the market; 2030 targets for dispatchable solar plants are 5¢/kWh for baseload CSP and 10¢/kWh for peaker plants, as set by the United States Department of Energy. In response to this need, this study is targeting improvements in the power cycle precooler to reduce power block contribution to LCOE. This study considers a dry cooler, as CSP plants are sensitive to water consumption because many installations are slated for remote or arid locations where solar irradiance is very high, but water is scarce. Furthermore, the power block footprint for an sCO2 system is quite compact, especially as compared to a steam cycle. Therefore, there is interest in installing a more compact dry cooler that is proportional to the reduced footprint sCO2 power block, while conventional dry coolers are an order of magnitude larger. The competing goals of size, performance, and cost were considered in this study to develop a compact dry cooler that can easily be packaged with the power block, significantly reducing the installation and transport cost compared to the current state of the art, while maintaining or improving upon the heat transfer performance and impact on plant LCOE. This paper details the high-level findings of a large dry cooler sensitivity study for design point selection, design of the compact dry cooler, expected year-round performance for the dry cooler and the power cycle, and the predicted LCOE for a 30-year plant life. It was found that an aluminum heat exchanger core can be suitably designed to meet the pressure and temperature requirements for a precooler in an sCO2 recompression Brayton cycle. The dry cooler assembly was found to have improved heat transfer performance, allowing for increased cycle efficiencies and a reduced plant LCOE. When coupled with a centrifugal blower and compact transition duct, the dry cooler assembly was able to reduce the installation footprint by over 50%.

14 SOLAR ENERGY↗

DOE OSTI2020

Task 12 PV Sustainability - Environmental Life Cycle Assessment of Residential PV and Battery Storage Systems

Using a life cycle assessment (LCA), the environmental impacts from generating 1 kWh of electricity for self-consumption via a photovoltaic-battery system are determined. The system includes a 10 kWp multicrystalline-silicon photovoltaic (PV) system (solar irradiation about 1350 kWh/m2/year and annual yield 1000 kWh/kWp), an iron phosphate lithium-ion (LiFePO4) battery, and other components such as the control system, battery housing, and two inverters (one for the PV system and one for the battery system). Three options for the AC-coupled system with changing battery capacities (5, 10, or 20 kWh nominal capacity) are investigated. The environmental impacts are assessed using the indicators greenhouse gas emissions and cumulative energy demand (separated into total and non-renewable cumulative energy demand). In addition, the four most important impact categories for PV electricity - respiratory inorganics (particulate matter), acidification, energy carrier resource use, and minerals and metals resource use - are assessed according to the environmental footprint (EF) method. Data are drawn from the DETEC data DQRv2:2018, recent literature, and product details provided by manufacturers. The results show larger environmental impacts of PV-battery systems with increasing battery capacity; for capacities of 5, 10, and 20 kWh, the cumulative greenhouse gas emissions from 1 kWh of electricity generation for self-consumption via a PV-battery system are 80, 84, and 88 g CO 2- eq/kWh, respectively. The cumulative greenhouse gas emissions of PV electricity consumed directly or fed into the grid are 54 g CO 2 -eq/kWh. The corresponding total cumulative energy demands are 5.27, 5.40, and 5.50 MJ oil-eq/kWh, with non-renewable energy carriers contributing 1.16, 1.22, and 1.29 MJ oil-eq/kWh. In the investigated EF impact categories, we similarly observe a larger environmental burden with increasing battery capacity, except in the use of minerals and metals. Our sensitivity analyses show that using a nickel cobalt manganese oxide (NCM) lithium-ion battery, instead of an LiFePO 4 battery, leads to a comparable environmental impact in terms of greenhouse gas emissions and cumulative energy demand. However, the NCM battery increases the impact in the EF categories of acidification and respiratory inorganics by 7 and 10%, respectively, whereas energy carrier resource use decreases by 4% and minerals and metals resource use decrease by 1%. Using a copper indium selenium (CIS) PV panel instead of a multicrystalline-silicon decreases greenhouse gas emissions by 24%, non-renewable cumulative energy demand by 13%, and particulate matter emissions by 60% (the largest decrease). Furthermore, the calculated environmental impacts are sensitive to the assumed battery lifetime. A decrease from 5000 to 3000 charge cycles increases non-renewable cumulative energy demand by 24% and greenhouse gas emissions by 16%. Increasing from 5000 to 7000 charge cycles decreases the environmental impacts by 6% and 7% in terms of non-renewable cumulative energy demand and greenhouse gas emissions, respectively. A utility-scale battery system case study shows that using batteries to store PV electricity overproduction reduces greenhouse gas emissions compared to using natural gas backup electricity generation.

14 SOLAR ENERGY↗

DOE OSTI2020

Visible-Light-Activated Photocatalytic Films toward Self-Cleaning Membranes

Membranes are among the most promising means of delivering increased supplies of fit-for-purpose water, but membrane fouling remains a critical issue restricting their widespread application. Coupling photocatalysis with membrane separation has been proposed as a potentially effective approach to reduce membrane fouling. However, commonly used materials in photocatalysis limit use of low-cost sources such as sunlight due to their large band gaps. There are few examples of in situ photocatalytic self-cleaning of membranes, with removal from the filtration system and ex situ illumination being more common. In this study, a visible-light-activated photocatalytic film prepared by nitrogen doping into the lattice of TiO 2 was deposited on commercial ceramic membranes via atomic layer deposition (ALD). The synergy between membrane separation and redox reactions between organic pollutants and reactive oxygen species (ROS) produced by the visible-light-activated layer offers a possibility for stable and sustainable membrane operation under in situ solar irradiation.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Photocatalytic Semi‐Hydrogenation of Acetylene to Polymer‐Grade Ethylene with Molecular and Metal–Organic Framework Cobaloximes

Abstract The semi‐hydrogenation of acetylene in ethylene‐rich gas streams is a high‐priority industrial chemical reaction for producing polymer‐grade ethylene. Traditional thermocatalytic routes for acetylene reduction to ethylene, despite progress, still require high temperatures and high H 2 consumption, possess relatively low selectivity, and use a noble metal catalyst. Light‐powered strategies are starting to emerge, given that they have the potential to use directly the abundant and sustainable solar irradiation, but are ineffective. Here an efficient, >99.9% selective, visible‐light powered, catalytic conversion of acetylene to ethylene is reported. The catalyst is a homogeneous molecular cobaloxime that operates in tandem with a photosensitizer at room temperature and bypasses the use of non‐environmentally friendly and flammable H 2 gas feed. The reaction proceeds through a cobalt‐hydride intermediate with ≈100% conversion of acetylene under competitive (ethylene co‐feed) conditions after only 50 min, and with no evolution of H 2 or over‐hydrogenation to ethane. The cobaloxime is further incorporated as a linker in a metal–organic framework; the result is a heterogeneous catalyst for the conversion of acetylene under competitive (ethylene co‐feed) conditions that can be recycled up to six times and remains catalytically active for 48 h, before significant loss of performance is observed.

Stone, Aaron E. B. S.↗

DOE OSTI2025

Bio‐Inspired Cascade Photocatalysis on Fe Single‐Atom Carbon Nitride Upcycles Plastic Wastes for Effective Acetic Acid Production

Plastic imposes a critical threat to the environment, ecosystems, and human health because of the low utilization efficiency of plastics. Here, we demonstrate a sustainable, highly efficient cascade photocatalysis for upcycle plastics to value-added acetic acid using Fe single-atom catalysts (Fe@C 3 N 4 SAC) at ambient conditions. Inspired by Phanerochaete chrysosporium microbial, the defective Fe@C 3 N 4 SAC acts as a bifunctional cascade photocatalyst for both Fenton-like and CO 2 reduction reactions. During the reaction, hydroxyl radicals (*OH) form and subsequently oxidize plastics into CO 2 intermediates. These CO 2 intermediates are then photo-reduced to CH 3 COOH on the same catalyst via cascade photocatalysis. The mechanism is confirmed by in situ multimodal microscopy and spectroscopies, with density functional theory calculations. A state-of-art CH 3 COOH yield of 63.8 mg h −1 gcat −1 from PVC, 12.7 mg h −1 gcat −1 from PE, 5.4 mg h −1 gcat −1 from PET, and 5.3 mg h −1 gcat −1 from PP are directly obtained under AM1.5G solar irradiation and further validated under real sunlight (≈0.6 sun), achieving 5.6 mg h −1 gcat −1 from PET, using low-cost Fe@C 3 N 4 SAC in a sealed reactor by enhancing the photon transport and utilization efficiency. The techno-economic analysis shows it is promising to practically mitigate plastic based on broader social welfare assessments.

cascade photocatalysis↗

DOE OSTI2023

Designing Heteroatom-Codoped Iron Metal–Organic Framework for Promotional Photoreduction of Carbon Dioxide to Ethylene

Rational engineering active sites and vantage defects of catalysts are promising but grand challenging task to enhance photoreduction CO 2 to high value-added C2 products. In this study, we designed an N,S-codoped Fe-based MIL-88B catalyst with well-defined bipyramidal hexagonal prism morphology via a facile and effective process, which was synthesized by addition of appropriate 1,2-benzisothiazolin-3-one (BIT) and acetic acid to the reaction solution. Under simulated solar irradiation, the designed catalyst exhibits high C 2 H 4 evolution yield of 17.7 μmol g -1 ∙h, which has been rarely achieved in photocatalytic CO 2 reduction process. The synergistic effect of Fe-N coordinated sites and reasonable defects in the N,S-codoped photocatalyst can accelerate the migration of photogenerated carriers, resulting in high electron density, and this in turn helps to facilitate the formation and dimerization of C-C coupling intermediates for C 2 H 4 effectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Highly Selective Photoelectroreduction of Carbon Dioxide to Ethanol over Graphene/Silicon Carbide Composites

Using sunlight to produce valuable chemicals and fuels from carbon dioxide (CO 2 ), i.e., artificial photosynthesis (AP) is a promising strategy to achieve solar energy storage and a negative carbon cycle. However, selective synthesis of C 2 compounds with a high CO 2 conversion rate remains challenging for current AP technologies. Here, we performed CO 2 photoelectroreduction over a graphene/silicon carbide (SiC) catalyst under simulated solar irradiation with ethanol (C 2 H 5 OH) selectivity of>99 % and a CO 2 conversion rate of up to 17.1 mmol g cat -1 h -1 with sustained performance. Experimental and theoretical investigations indicated an optimal interfacial layer to facilitate the transfer of photogenerated electrons from the SiC substrate to the few-layer graphene overlayer, which also favored an efficient CO 2 to C 2 H 5 OH conversion pathway.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Chapter 7: Predictive Analytics in Future Power Systems: A Panorama and State-of-the-Art of Deep Learning Applications

The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves - potentially at the cost of suboptimal economic conditions - in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Review of Onsite Temperature and Solar Forecasting Models to Enable Better Building Design and Operations

Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. Additionally, this paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Structure-dependent mechanisms for plastic degradation in the marine environment

The visible and invisible accumulation of waste plastic in our oceans is a consequence of rapidly increasing inputs and very slow degradation rates. In general, this combination of effects results in long residence times. Nevertheless, the abiological degradation rate of a specific synthetic plastic varies widely, depending on its structural characteristics at the molecular, mesoscopic, and macroscopic levels. This chapter summarizes factors that determine these rates of degradation, and describes their principal mechanisms, for several of the major types of waste plastic found in the oceans. Mechanical forces cause cracking and flaking that break macroplastics into microplastics and even smaller fragments with increasing surface-to-volume ratios. Chemical degradation rates increase with surface area, influenced by intrinsic variations in crystallinity and the presence of additives, as well as extrinsic factors such as temperature, pH, and UV light intensity. Since polyolefin-based plastics are less dense than seawater and lack hydrolysable bonds in their polymer backbones, they accumulate close to the surface of the ocean where photo-oxidation is the principal cause of C–C bond cleavage in the polymer backbone. In contrast, polyesters and polyamides are denser than seawater and they sink, experiencing less exposure to solar irradiation. However, they can also degrade by slow hydrolysis of the C–O or C–N bonds intrinsic to the polymer structures.

Maciulis, Nicholas A.↗

DOE OSTI2021

Resilience and economics of microgrids with PV, battery storage, and networked diesel generators

Current designs and assessments of microgrids have ignored component reliability, leading to significant errors in predicting a microgrid’s performance while islanded. Existing life cycle cost studies on hybrid microgrids—which combine photovoltaics (PV), battery storage and networked emergency diesel generators—also have not identified all the potential economic opportunities. Reducing the number of emergency diesel generators through reliance on PV and battery, retail bill savings, and demand response and wholesale market revenue streams are all important. This paper provides a new statistical methodology that calculates the impact of distributed energy reliability and variability on a microgrid’s performance and a novel use of the optimization platform REopt to explore multiple cost savings and revenue streams. We examine the impacts for microgrids in California, Maryland, and New Mexico and show that a hybrid microgrid is a more resilient and cost-effective solution than a diesel-only system. Under realistic conditions, a hybrid microgrid can provide higher system reliability when islanded and have a lower life cycle cost under multiple market conditions than a traditional diesel generator-based system. The improved performance of the hybrid system is resilient to conditions experienced over the last 20 years in solar irradiance and sees little degradation in performance immediately after a hurricane. The cost savings to provide this more resilient backup power system as compared to a diesel-only microgrid are significant. The net present cost for a hybrid microgrid is 19% lower in New Mexico and 35% lower in Maryland than a diesel-only microgrid. In California, the net present cost of the hybrid microgrid is negative because, unlike a diesel-only microgrid, a hybrid microgrid has lower life cycle costs than the power costs without a microgrid.

25 ENERGY STORAGE↗

DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗