Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Marco Pierro ◽  
Francesco Bucci ◽  
Matteo De Felice ◽  
Enrico Maggioni ◽  
Alessandro Perotto ◽  
...  
Keyword(s):  
Power Generation ◽  
Stochastic Models ◽  
Solar Power ◽  
Weather Prediction ◽  
Wrf Model ◽  
Machine Learning Techniques ◽  
Electricity Grid ◽  
Clear Sky ◽  
Pv Power Generation ◽  
Power Forecasting

Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of resource variability caused by high solar power penetration into the electricity grid. Two main methods are currently used for PV power generation forecast: (i) a deterministic approach that uses physics-based models requiring detailed PV plant information and (ii) a data-driven approach based on statistical or stochastic machine learning techniques needing historical power measurements. The main goal of this work is to analyze the accuracy of these different approaches. Deterministic and stochastic models for day-ahead PV generation forecast were developed, and a detailed error analysis was performed. Four years of site measurements were used to train and test the models. Numerical weather prediction (NWP) data generated by the weather research and forecasting (WRF) model were used as input. Additionally, a new parameter, the clear sky performance index, is defined. This index is equivalent to the clear sky index for PV power generation forecast, and it is here used in conjunction to the stochastic and persistence models. The stochastic model not only was able to correct NWP bias errors but it also provided a better irradiance transposition on the PV plane. The deterministic and stochastic models yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively.

scholarly journals Building the Sun4Cast System: Improvements in Solar Power Forecasting

2018 ◽  
Vol 99 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Branko Kosović ◽  
Tara Jensen ◽  
Jeffrey K. Lazo ◽  
Jared A. Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Value Chain ◽  
Solar Irradiance ◽  
Collaborative Design ◽  
Solar Power ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Solar Array ◽  
Solar Power Forecasting ◽  
Power Forecasting

Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

Economically Optimal Solar Power Generation

2020 ◽  
pp. 254-290
Author(s):  
Sana Badruddin ◽  
Cameron Ryan Robertson-Gillis ◽  
Janice Ashworth ◽  
David J. Wright
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Solar Power ◽  
Electricity Consumption ◽  
The Other ◽  
Electricity Prices ◽  
Electricity Grid ◽  
The Public ◽  
Solar Power Generation ◽  
Electricity Bill

The Ottawa Renewable Energy Cooperative is considering installing solar modules on the roofs of two buildings while they stay connected to the public electricity grid. Solar power produced over their own needs would be sent to the public electricity grid for a credit on their electricity bill. When they need more power than they are generating, these buildings would purchase electricity from the grid. In addition to paying for the electricity they purchase, they would be subject to a “demand charge” that applies each month to the hour during which their consumption is at a peak for that month. Any electricity consumed during that peak hour would be charged at a rate about 100 times the rate for other hours. The case addresses three questions: (1) Is it profitable for these organizations to install solar on their roofs? (2) Can profitability be increased by adding a battery? and (3) How sensitive is profitability to uncertainty in future electricity prices? The case shows how the answers to these questions depend on the profile of hourly electricity consumption during the day, which is very different from one building to the other.

scholarly journals A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6447
Author(s):  
Ling Liu ◽  
Fang Liu ◽  
Yuling Zheng
Keyword(s):  
Power Generation ◽  
Fuzzy Model ◽  
Redundant Information ◽  
Short Term ◽  
Forecasting Method ◽  
Input Variables ◽  
Takagi Sugeno ◽  
Pv Power Generation ◽  
Fuzzy Subsets ◽  
Power Forecasting

Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based Takagi-Sugeno (T-S) fuzzy model. Firstly, the correlation analysis is used to filter redundant information. Furthermore, a T-S fuzzy model, which integrates fuzzy c-means (FCM) for the fuzzy division of input variables and DBN for fuzzy subsets forecasting, is developed. Finally, the proposed method is compared to a benchmark DBN method and the T-S fuzzy model in case studies. The numerical results show the feasibility and flexibility of the proposed ultra-short-term PV power forecasting approach.

scholarly journals Climate change impacts on solar power generation and its spatial variability in Europe based on CMIP6

2021 ◽  
Vol 12 (4) ◽  
pp. 1099-1113
Author(s):  
Xinyuan Hou ◽  
Martin Wild ◽  
Doris Folini ◽  
Stelios Kazadzis ◽  
Jan Wohland
Keyword(s):  
Climate Change ◽  
Power Generation ◽  
Spatial Variability ◽  
Cloud Cover ◽  
Climate Change Impacts ◽  
Solar Pv ◽  
Clear Sky ◽  
Generation Structure ◽  
Recent Climate ◽  
Pv Power Generation

Abstract. Solar photovoltaics (PV) plays an essential role in decarbonizing the European energy system. However, climate change affects surface solar radiation and will therefore directly influence future PV power generation. We use scenarios from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) for a mitigation (SSP1-2.6) and a fossil-fuel-dependent (SSP5-8.5) pathway in order to quantify climate risk for solar PV in Europe as simulated by the Global Solar Energy Estimator (GSEE). We find that PV potential increases by around 5 % in the mitigation scenario, suggesting a positive feedback loop between climate change mitigation and PV potential. While increased clear-sky radiation and reduced cloud cover go hand in hand in SSP1-2.6, the effect of a decrease in clear-sky radiation is outweighed by a decrease in cloud cover in SSP5-8.5, resulting in an increase in all-sky radiation. Moreover, we find that the seasonal cycle of PV generation changes in most places, as generation grows more strongly in winter than in summer (SSP1-2.6) or increases in summer and declines in winter (SSP5-8.5). We further analyze climate change impacts on the spatial variability of PV power generation. Similar to the effects anticipated for wind energy, we report an increase in the spatial correlations of daily PV production with large inter-model agreement yet relatively small amplitude, implying that PV power balancing between different regions in continental Europe will become more difficult in the future. Thus, based on the most recent climate simulations, this research supports the notion that climate change will only marginally impact renewable energy potential, while changes in the spatiotemporal generation structure are to be expected and should be included in power system design.

scholarly journals Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

2021 ◽  
Vol 309 ◽  
pp. 01163
Author(s):  
K. Anuradha ◽  
Deekshitha Erlapally ◽  
G. Karuna ◽  
V. Srilakshmi ◽  
K. Adilakshmi
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Random Forest ◽  
Regression Models ◽  
Solar Power ◽  
Machine Learning Techniques ◽  
Pv Systems ◽  
Solar Power Generation ◽  
Learning Techniques ◽  
Solar Power Forecasting

Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

Economically Optimal Solar Power Generation

2021 ◽  
pp. 385-415
Author(s):  
Sana Badruddin ◽  
Cameron Ryan Robertson-Gillis ◽  
Janice Ashworth ◽  
David J. Wright
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Solar Power ◽  
Electricity Consumption ◽  
The Other ◽  
Electricity Prices ◽  
Electricity Grid ◽  
The Public ◽  
Solar Power Generation ◽  
Electricity Bill

The Ottawa Renewable Energy Cooperative is considering installing solar modules on the roofs of two buildings while they stay connected to the public electricity grid. Solar power produced over their own needs would be sent to the public electricity grid for a credit on their electricity bill. When they need more power than they are generating, these buildings would purchase electricity from the grid. In addition to paying for the electricity they purchase, they would be subject to a “demand charge” that applies each month to the hour during which their consumption is at a peak for that month. Any electricity consumed during that peak hour would be charged at a rate about 100 times the rate for other hours. The case addresses three questions: (1) Is it profitable for these organizations to install solar on their roofs? (2) Can profitability be increased by adding a battery? and (3) How sensitive is profitability to uncertainty in future electricity prices? The case shows how the answers to these questions depend on the profile of hourly electricity consumption during the day, which is very different from one building to the other.

scholarly journals Skill of Direct Solar Radiation Predicted by the ECMWF Global Atmospheric Model over Australia

2014 ◽  
Vol 53 (11) ◽  
pp. 2571-2588 ◽  
Author(s):  
Alberto Troccoli ◽  
Jean-Jacques Morcrette
Keyword(s):  
Solar Radiation ◽  
Power Plants ◽  
Solar Power ◽  
Weather Prediction ◽  
Global Radiation ◽  
Atmospheric Model ◽  
Direct Solar Radiation ◽  
Efficient Production ◽  
Clear Sky ◽  
Sky Conditions

AbstractPrediction of direct solar radiation is key in sectors such as solar power and agriculture; for instance, it can enable more efficient production of energy from concentrating solar power plants. An assessment of the quality of the direct solar radiation forecast by two versions of the European Centre for Medium-Range Weather Forecasts (ECMWF) global numerical weather prediction model up to 5 days ahead is carried out here. The performance of the model is measured against observations from four solar monitoring stations over Australia, characterized by diverse geographic and climatic features, for the year 2006. As a reference, the performance of global radiation forecast is carried out as well. In terms of direct solar radiation, while the skill of the two model versions is very similar, and with relative mean absolute errors (rMAEs) ranging from 18% to 45% and correlations between 0.85 and 0.25 at around midday, their performance is substantially enhanced via a simple postprocessing bias-correction procedure. There is a marked dependency on cloudy conditions, with rMAEs 2–4 times as large for very cloudy-to-overcast conditions relative to clear-sky conditions. There is also a distinct dependency on the background climatic clear-sky conditions of the location considered. Tests made on a simulated operational setup targeting three quantiles show that direct radiation forecasts achieve potentially high scores. Overall, these analyses provide an indication of the potential practical use of direct irradiance forecast for applications such as solar power operations.

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