Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

Sangrody, Hossein; Zhou, N.; Zhang, Ziang

doi:10.1109/access.2020.2999903

Cited by 44 publications

(9 citation statements)

References 32 publications

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“…The dataset used in this proposed model is extracted from various Machine Learning algorithms. Sangrody et al [15] had designed a similarity based model for forecasting the day ahead solar photo voltaic generation. The author proposed three forecasting models namely basic Similarity Based Forecasting Model (SBFM), categorized Similarity Based Forecasting Model (SBFM) and hierarchical Similarity Based Forecasting Model (SBFM).…”

Section: Recent Research Resultsmentioning

confidence: 99%

Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

Geethamahalakshmi¹,

Kalaiarasi²,

Nageswari³

2022

Intelligent Automation &Amp; Soft Computing

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Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in the quantity of solar rays from place to place. This apprehension is overcome by following the technical methods of employing latest technology is determining the optimal position to harvest the solar power at the high rate and forecasting the power generation effectively. This paper proposes a novel hybrid methodology of employing fuzzy based controller to determine the Maximum Power Point Tracking (MPPT) in solar power generation and employing Artificial Intelligence (AI) technology to perform high precision forecasting of power generation. The K-Nearest Neighbor algorithm is a least assumption algorithm is employed in predicting the energy level harvested in the solar Photovoltaic cells. The Artificial Intelligence considers the vital parameters of displacement direction of the sun, temperature, clearness index and humidity in the air. The performance analysis of the proposed methodology is compared with the IEEE standard bus and the prediction is proved to be more precision with a maximum standard deviation of 0.06%.

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Section: Recent Research Resultsmentioning

confidence: 99%

Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

Geethamahalakshmi¹,

Kalaiarasi²,

Nageswari³

2022

Intelligent Automation &Amp; Soft Computing

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“…In [14], models are proposed for predicting the power of PV with a resolution of 5 minutes using weather variables with a resolution of 1 hour, which improves the accuracy of the forecast.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Improved control of energy consumption by a photovoltaic system equipped with a storage device to meet the needs of a local facility

Shavolkin

Shvedchykova

Jasim

2021

EEJET

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+This paper has considered improving the management of energy consumption by a photovoltaic system with a storage device for a local object connected to the network. The aim of the study is to reduce expenditures when paying for electricity consumed from the network, when loading an object, independent of the time of year, and to eliminate energy generation to the grid. An energy generation control algorithm has been improved whereby the state of battery charge during the day is based on a forecast. That could reduce electricity consumption at night with better utilization of rechargeable battery and photovoltaic battery power during the day. It is proposed to use autonomous operation by disconnecting from the network during peak tariff hours and during the day with enough energy generation by a photovoltaic battery. This would ensure the normal functioning of an object in the event of a possible deterioration in the quality of voltage in the network while reducing the loss of energy in the inverter. Predictive control of the expected battery charge at the next checkpoint (at 0.5 hours or less between control points) has been proposed. A control system structure has been developed whereby a rechargeable battery current is set depending on an operational mode, the tariff zone, and the projected generation by a photovoltaic battery while reducing the modulation frequency under an autonomous mode. In this case, the modes are switched and the structure is changed taking into consideration the state of battery charge. Simulation in the daily cycle has shown the possibility of reducing the cost of electricity consumed from the network by 1.7‒8 times at two or three tariff rates. Simulation of electromagnetic processes in the system confirms acceptable regulation indicators when switching the structure, as well as a decrease in the energy loss in an inverter under an autonomous mode by up to 40 %

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“…Based on correlations among weather variables [9], [14], Marquez et al [15] concludes that sky (cloud) cover, (probability of) precipitation, (maximum and minimum) temperatures, and cos(ߠ ௌ ) are overwhelmingly and critically important to enhance the forecasting accuracy. Excluding sky cover from a list of commonly available weather data, Sangrody et al [16] and Qing et al [17] found that temperature and humidity had the strongest influence on accuracy of irradiance forecasts.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Prediction of Distributed Regional-Scale Photovoltaic Power

2023

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Day-ahead forecasts are required by electricity market investors to make informed decisions on the trading floor. Whereas it is relatively easier to predict the performance of a few large-scale photovoltaic (PV) systems, a large number of small-scale PV systems with a wide geographical spread poses more challenges because they are often not metered for real-time monitoring. This paper proposes an artificial neural network (ANN)-based model to achieve regional-scale day-ahead PV power forecasts based on weather variables from numerical weather predictions (excluding solar irradiance) as inputs. The model was first implemented by dividing a region into clusters and selecting a representative site for each cluster using data dimension reduction algorithms. Solar irradiance forecasts were then generated for each representative PV system and the corresponding PV power was simulated. The cluster power output was obtained using a linear upscaling model and summed to produce regional-scale power forecasts. The model's accuracy is validated using power generation data of several distributed systems in California. The results show at least a 29-percent root mean square error reduction over the benchmarking models.INDEX TERMS Artificial neural network, behind-the-meter, day-ahead forecast, distributed solar, k-means clustering, principal component analysis.

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Similarity-Based Models for Day-Ahead Solar PV Generation Forecasting

Cited by 44 publications

References 32 publications

Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

Improved control of energy consumption by a photovoltaic system equipped with a storage device to meet the needs of a local facility

Day-Ahead Prediction of Distributed Regional-Scale Photovoltaic Power

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