A Concise Overview on Solar Resource Assessment and Forecasting

Yang, Di; Wang, Wenting; Xia, Xiang-ao

doi:10.1007/s00376-021-1372-8

Cited by 48 publications

(4 citation statements)

References 70 publications

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“…Pinpointing optimal sites for solar farms involves diverse methodologies, such as MCDA (a technique for order of preference by similarity to an ideal solution (TOPSIS), ordered weight averaging (OWA), and fuzzy AHP) [11], solar resource assessment [73], viewshed analysis [74], solar pathfinder analysis [75], Boolean-fuzzy logic model [61], the Dempster-Shafer method [76], and many more. Integrating machine learning and AI algorithms [77] also proves advantageous for renewable energy planning and microgrid development.…”

Section: Discussionmentioning

confidence: 99%

Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration

Gacu,

Garcia,

Fetalvero

et al. 2023

Energies

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Sibuyan Island is experiencing a significant increase in electricity demand due to population growth, urbanization, and industrial development. The island plans to use solar energy, recognizing its abundance and renewable nature; thus, this study was conducted to visualize the spatial distribution of solar exploration suitability using a geographic information system (GIS). Various criteria, including climatology, location, geography, meteorology, and disaster susceptibility, were considered in the assessment. Parameters affected by government policies, such as protected areas, proximity to rivers, roads and faults, ancestral domains, and proclaimed watersheds, were also considered. Parameters were weighted, and suitability levels were highlighted using AHP. The study revealed that about 5.88% (2674.06 km2) of the island was categorized as highly suitable for a solar farm, 34.99% (15,908.21 km2) as suitable, 2.49% (1129.95 km2) as moderately suitable, and the majority, 56.64% (25,754.47 km2), was considered not suitable for solar projects. A solar power suitability map was developed as a reference for local governments and residents in establishing solar PV systems in their respective sites, thus maximizing the full potential of their land. The study also directs future solar power exploration studies in Sibuyan Island, supporting ongoing efforts to maximize solar energy utilization.

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Section: Discussionmentioning

confidence: 99%

Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration

Gacu,

Garcia,

Fetalvero

et al. 2023

Energies

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“…To understand whether the energy required for cooking processes can be supplied by solar systems at a given location, and to design or size such systems, it is necessary to quantify the amount of solar irradiance available for energy conversion over a period of interest. Such information can be obtained by performing a solar resource assessment, which requires historical irradiance data from local ground-based measurements, meteorological model outputs or satellite measurements [24,25]. The data and methodologies used for those assessments should (i) ensure reliability; (ii) account for different time-scale phenomena and intra/inter annual patterns and trends by considering long-term data; and (iii) have low uncertainty.…”

Section: Solar Energy Availabilitymentioning

confidence: 99%

Solar Resource and Energy Demand for Autonomous Solar Cooking Photovoltaic Systems in Kenya and Rwanda

Cardoso,

Couto,

Costa

et al. 2023

Solar

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The challenges associated with traditional cooking methods in African countries, particularly the use of firewood and charcoal, which have negative impacts on the environment, health and human and economic development and safety, are addressed in this work. Given the high annual solar irradiation on the African continent, photovoltaic-powered electric cooking alternatives, such as electric pressure cookers (EPCs), are identified as a potential efficient, clean and affordable cooking solution. This work focuses on the potential of standalone solar electric cookers for use in rural African locations, namely, if this type of solution can satisfy cooking demand. Surveys and experimental data from several households in two different countries (Rwanda and Kenya) were collected. Specifically, the researchers performed a survey regarding cooking habits and an experimental campaign to determine real energy consumption profiles of EPCs. The main results are analyzed and discussed in this work. An assessment of the solar power capability to directly supply the EPCs’ energy demand, as determined from the experimental data, is performed. The findings indicate that, for the most commonly prepared food types, using EPCs saves considerable time in comparison with traditional cooking methods. In Rwanda, time savings range from 55% to 84%, while in Kenya, the time saved varies from 9% to 64%. Results show that, even for scenarios with high installed solar capacity, storage solutions are required to enable the PV-powered EPC system to supply more than 50% of meal demand.

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“…Although the significance of the physical model chain is implied [7], [8], this study seeks to employ machine learning to address aspects of the PV generation that are challenging for physical models. For example, current physical models still use estimates for the calculation of various losses, including spectral and pollution losses due to a scarcity of relevant data.…”

Section: Introductionmentioning

confidence: 99%

Conversion of Solar Irradiance to Photovoltaic Power with Hybrid Model Chains

Xia,

Gao,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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Accurate solar resourcing and forecasting depend upon the ability to convert weather forecasts to photovoltaic (PV) power forecasts, which remains challenging till this day. This study explores the fusion of physical model chains and machine learning, to achieve improved irradiance-to-power conversion. The outcomes of some well-tested steps of a model chain are used as input features of machine learning models, so as to form a hybrid model with high precision and wide applicability. Within this framework, a comparative analysis is conducted among three potential machine-learning models, including the long short-term memory (LSTM) network, k-nearest neighbors, and gradient boosting regressor. The results indicate that the physical-LSTM hybrid model exhibits superior performance to other options, reaching a correlation coefficient of 0.997. In cases where specific modeling parameters are unavailable, the hybrid model can mitigate the reliance on PV design parameters while gaining a notable increase in irradiance-to-power conversion accuracy, thereby substantiating a robust underpinning for PV grid connection.

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A Concise Overview on Solar Resource Assessment and Forecasting

Cited by 48 publications

References 70 publications

Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration

Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration

Solar Resource and Energy Demand for Autonomous Solar Cooking Photovoltaic Systems in Kenya and Rwanda

Conversion of Solar Irradiance to Photovoltaic Power with Hybrid Model Chains

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