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LASSO & LSTM Integrated Temporal Model for Short-term Solar Intensity Forecasting
摘要： As a special form of the Internet of Things, Smart Grid is an internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model and machine learning. The proposed scheme first clusters data using k-means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the non-linear relationships, and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.
关键词： Internet of Things (IoT),Least absolute shrinkage and selection operator (LASSO),Short-term solar power forecasting,Long short term memory (LSTM),K-means++
更新于2025-09-10 09:29:36
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An Ensemble Framework For Day-Ahead Forecast of PV Output in Smart Grids
摘要： The uncertainty associated with solar photo-voltaic (PV) power output is a big challenge to design, manage and implement effective demand response and management strategies. Therefore, an accurate PV power output forecast is an utmost importance to allow seamless integration and a higher level of penetration. In this research, a neural network ensemble (NNE) scheme is proposed, which is based on particle swarm optimization (PSO) trained feedforward neural network (FNN). Five different FFN structures with varying network complexities are used to achieve the diverse and accurate forecast results. These results are combined using trim aggregation after removing the upper and lower forecast error extremes. Correlated variables namely wavelet transformed historical power output of PV, solar irradiance, wind speed, temperature and humidity are applied as inputs to the multivariate NNE. Clearness index is used to classify days into clear, cloudy and partial cloudy days. Test case studies are designed to predict the solar output for these days selected from all seasons. The performance of the proposed framework is analyzed by applying training data set of different resolution, length and quality from seven solar PV sites of the University of Queensland, Australia. The forecast results demonstrate that the proposed framework improves the forecast accuracy significantly in comparison with individual and benchmark models.
关键词： clear day (CD),solar irradiance,cloudy day (CLD),clearness index,PV power output forecasting,ensemble network (EN),neural network ensemble (NNE),partially cloudy day (PCD),particle swarm optimization
更新于2025-09-10 09:29:36
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Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm
摘要： Due to increasing energy demand and fossil fuel costs in island and remote areas, renewable energy resources are becoming increasingly attractive. The hybridization of these resources can help overcome their variability and intermittency and improve e?ciency. Many independent hybrid renewable energy systems are used in remote and island areas for which weather data often is unavailable. To increase the accuracy of size optimization of such systems, more accurate weather data is needed and the use of weather forecasting data is helpful. In this article, a new hybrid optimization algorithm is proposed for the optimal sizing of a stand-alone hybrid solar and wind energy system based on three algorithms: chaotic search, harmony search and simulated annealing. To improve the accuracy of the size optimization algorithm results, weather forecasting is used along with arti?cial neural networks for solar radiation, ambient temperature, and wind speed forecasting. The main objective function of minimizing the total life cycle cost is used to assess the feasibility of the hybrid renewable energy system accounting for system reliability. The reliability of the system is assessed by the loss of power supply probability parameter. The new method is tested for the electrical load of the city of Khorasan, Iran. The results are compared with those obtained by the proposed algorithm (harmony search and simulated annealing-based arti?cial neural networks). The simulation results demonstrate the advantages of utilizing the hybrid optimization algorithm with weather forecasting data for a stand-alone hybrid renewable energy system.
关键词： Optimization,Stand-alone hybrid solar and wind energy system,Hybrid algorithm,Weather forecasting
更新于2025-09-10 09:29:36
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[IEEE 2018 2nd International Conference On Electrical Engineering (EECON) - Colombo, Sri Lanka (2018.9.28-2018.9.28)] 2018 2nd International Conference On Electrical Engineering (EECon) - Application of Machine Learning Algorithms for Solar Power Forecasting in Sri Lanka
摘要： Reliability and stability of a power system get decrease with the integration of large proportion of renewable energy. Renewable sources such as solar and wind are highly intermittent, and it is difficult to maintain system stability with intolerable proportion of renewable energy injection. Solar power forecasting can be used to improve system stability by providing approximated future power generation to system control engineers and it will facilitate dispatch of hydro power plants in an optimum way. Machine Learning (ML) algorithms have shown great performance in time series forecasting and hence can be used to forecast power using weather parameters as model inputs. This paper presents the application of several ML algorithms for solar power forecasting in Buruthakanda solar park situated in Hambantota, Sri Lanka. The forecasting performance of implemented ML algorithms is compared with Smart Persistence (SP) method and the research shows that the ML models outperforms SP model.
关键词： solar power forecasting,solar power in Sri Lanka,machine learning for forecasting,renewable energy
更新于2025-09-09 09:28:46
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[Lecture Notes in Computer Science] Web and Big Data Volume 11268 (APWeb-WAIM 2018 International Workshops: MWDA, BAH, KGMA, DMMOOC, DS, Macau, China, July 23–25, 2018, Revised Selected Papers) || Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm
摘要： In this paper, the combination of Near-Infrared (NIR) spectroscopy and a novel forecasting algorithm called XGBoost was proposed for food internal quality evaluation. First, the original NIR spectral data was preprocessed by Savitzky-Golay smoothing method to reduce the influence of noises. Secondly, the preprocessed spectra was submitted to PCA to extract essential information. Finally, the model was established by using the XGBoost algorithm. The performance of the proposed model was examined by comparing with different models including back propagation neural network (BPNN) and support vector regression (SVR). The results showed that the new proposed model outperformed other two models and this XGBoost-based tool was suitable for food internal quality control.
关键词： Internal quality forecasting,Food,NIR spectroscopy,XGBoost
更新于2025-09-09 09:28:46
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[IEEE 2018 2nd International Conference on Engineering Innovation (ICEI) - Bangkok, Thailand (2018.7.5-2018.7.6)] 2018 2nd International Conference on Engineering Innovation (ICEI) - Forecasting Self-Consumption Solar Power Capacity of Industry and Business Sector in Thailand: a System Dynamic Model
摘要： The future of solar Photovoltaic (PV) technology is bright. Not only solar PV is a clean energy but also has its price dropped and has its efficiency improved significantly since 1975. Thus, investing in solar PV is feasible and attractive nowadays. In Thailand, industrial and business sector are the largest power consumers which have an opportunity to reduce electricity cost and promote Corporate Social Responsibility (CSR) by using solar PV. So, installing solar PV as Isolated Power Supply system (IPS) of companies in the industrial sector reduces electricity consumption from the grid. This would cause a problem in demand forecasting for electricity in the grid. Through a questionnaire survey, this research aims to develop a system dynamic model to understand the dynamic behavior of factors that affect the solar PV capacity, and forecast the monthly solar PV capacity growth from 2018 to 2022 of the industry and business sector in Thailand.
关键词： Electricity generation capacity,System dynamics,Solar PV,Forecasting
更新于2025-09-09 09:28:46
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A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique
摘要： Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. This study introduces a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL). A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL’s Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.
关键词： Cloud fraction,Global horizontal irradiance,Smart persistence,Solar forecasting,Cloud albedo
更新于2025-09-09 09:28:46
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Analysis of the Potential of Solar Energy Development inSaudi Arabia
摘要： Saudi Arabia has been exploring the potential of renewable energy for many years. Saudi authorities, scientists, and researchers view the generation of renewable energy as a viable long-term energy strategy. Despite this, because Saudi Arabia is one of the leading oil producing nations and relies heavily on it as a form of energy and source of income, solar energy has not been given much serious consideration. However, it has become more and more evident that for the continuing prosperity of the nation and the inevitable gradual decline in long-term oil production, it is essential to explore and invest in alternative energy sources. The main objectives of this research are: i) to establish the potential of solar energy generation as a suitable, cost-effective alternative to petroleum products. ii) to establish the potential for maximizing renewable power generation to support the supply grid. This paper presents an examination of various economic and technological aspects of generating solar energy in Saudi Arabia. Using some existing data on the amount of solar radiation, a seasonal multiple linear forecasting method is used to generate forecasts for electric energy generation potential for 32 cities. Results of this research demonstrate the desirability and economic feasibility of installing solar panel farms and constructing distribution lines.
关键词： Quantitative Forecasting,Photovoltaic Generator,Renewable energy,Levelized Cost of Electricity (LCOE) generation model
更新于2025-09-09 09:28:46
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Cloud shadow speed sensor
摘要： Changing cloud cover is a major source of solar radiation variability and poses challenges for the integration of solar energy. A compact and economical system is presented that measures cloud shadow motion vectors to estimate power plant ramp rates and provide short-term solar irradiance forecasts. The cloud shadow speed sensor (CSS) is constructed using an array of luminance sensors and a high-speed data acquisition system to resolve the progression of cloud passages across the sensor footprint. An embedded microcontroller acquires the sensor data and uses a cross-correlation algorithm to determine cloud shadow motion vectors. The CSS was validated against an artificial shading test apparatus, an alternative method of cloud motion detection from ground-measured irradiance (linear cloud edge, LCE), and a UC San Diego sky imager (USI). The CSS detected artificial shadow directions and speeds to within 15? and 6 % accuracy, respectively. The CSS detected (real) cloud shadow directions and speeds with average weighted root-mean-square difference of 22? and 1.9 m s?1 when compared to USI and 33? and 1.5 m s?1 when compared to LCE results.
关键词： cloud motion vectors,solar irradiance,cloud shadow speed sensor,forecasting,solar energy
更新于2025-09-09 09:28:46
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[ASME ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - Boston, Massachusetts, USA (Sunday 2 August 2015)] Volume 2A: 41st Design Automation Conference - Solar Power Ramp Events Detection Using an Optimized Swinging Door Algorithm
摘要： Solar power ramp events (SPREs) significantly influence the integration of solar power on non-clear days and threaten the reliable and economic operation of power systems. Accurately extracting solar power ramps becomes more important with increasing levels of solar power penetrations in power systems. In this paper, we develop an optimized swinging door algorithm (OpSDA) to enhance the state of the art in SPRE detection. First, the swinging door algorithm (SDA) is utilized to segregate measured solar power generation into consecutive segments in a piecewise linear fashion. Then we use a dynamic programming approach to combine adjacent segments into significant ramps when the decision thresholds are met. In addition, the expected SPREs occurring in clear-sky solar power conditions are removed. Measured solar power data from Tucson Electric Power is used to assess the performance of the proposed methodology. OpSDA is compared to two other ramp detection methods: the SDA and the L1-Ramp Detect with Sliding Window (L1-SW) method. The statistical results show the validity and effectiveness of the proposed method. OpSDA can significantly improve the performance of the SDA, and it can perform as well as or better than L1-SW with substantially less computation time.
关键词： Dynamic programming,solar power ramp events,ramp forecasting,swinging door algorithm,Tucson Electric Power
更新于2025-09-09 09:28:46