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Reconciling solar forecasts: Sequential reconciliation
摘要： When forecasting hierarchical photovoltaic (PV) power generation in a region and/or over several forecast horizons, reconciliation is needed to ensure the lower-level forecasts add up exactly to the upper-level forecasts. Previously in “Reconciling solar forecasts: Geographical hierarchy” [Sol. Energy 146 (2017) 276–286] and “Reconciling solar forecasts: Temporal hierarchy” [Sol. Energy 158 (2017) 332–346], forecast reconciliation has been demonstrated for geographical and temporal hierarchies, separately. This article follows such frameworks and extends the reconciliation to spatio-temporal cases. More specifically, sequential reconciliation is used for operational day-ahead forecasting of 318 PV systems in California. It is shown that by using sequential reconciliation, aggregate-consistent forecasts can be obtained across both the geographical and temporal hierarchies. In addition, the forecast accuracy can be further improved from that of the single-hierarchy cases.
关键词： Forecast reconciliation,Numerical weather prediction,Operational forecasting
更新于2025-09-23 15:23:52
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Outlier Events of Solar Forecasts for Regional Power Grid in Japan Using JMA Mesoscale Model
摘要： To realize the safety control of electric power systems under high penetration of photovoltaic power systems, accurate global horizontal irradiance (GHI) forecasts using numerical weather prediction models (NWP) are becoming increasingly important. The objective of this study is to understand meteorological characteristics pertaining to large errors (i.e., outlier events) of GHI day-ahead forecasts obtained from the Japan Meteorological Agency, for nine electric power areas during four years from 2014 to 2017. Under outlier events in GHI day-ahead forecasts, several sea-level pressure (SLP) patterns were found in 80 events during the four years; (a) a western edge of anticyclone over the Pacific Ocean (frequency per 80 outlier events; 48.8%), (b) stationary fronts (20.0%), (c) a synoptic-scale cyclone (18.8%), and (d) typhoons (tropical cyclones) (8.8%) around the Japanese islands. In this study, the four case studies of the worst outlier events were performed. A remarkable SLP pattern was the case of the western edge of anticyclone over the Pacific Ocean around Japan. The comparison between regionally integrated GHI day-ahead forecast errors and cloudiness forecasts suggests that the issue of accuracy of cloud forecasts in high- and mid-levels troposphere in NWPs will remain in the future.
关键词： outlier events,regional integration,global horizontal irradiance (GHI),photovoltaic (PV) power generation,numerical weather prediction (NWP),day-ahead forecast
更新于2025-09-23 15:23:52
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[IEEE 2018 Power Systems Computation Conference (PSCC) - Dublin, Ireland (2018.6.11-2018.6.15)] 2018 Power Systems Computation Conference (PSCC) - Annual Evaluation of Supply-Demand with BESS Charging/Discharging Schedule and UC Updating Based on Intraday Forecasted PV Power Outputs
摘要： In recent years, photovoltaic (PV) systems have been installed in Japan at an accelerated rate. The application of PV generation forecasts and the utilization of energy storage devices in power system operation are essential to reduce supply–demand imbalances and enable the use of more PV energy without curtailment. In this paper, assuming extremely high PV generation after 2030, we focus on the coordinated operation of a battery energy storage system (BESS) and conventional power plants. We propose a method of determining and updating the BESS charging/discharging schedule and generator unit commitment based on the day-ahead and intraday PV generation forecasts. We present an evaluation of this method based on the results of numerical simulations conducted for one year on a bulk power system model to demonstrate the effectiveness with which it reduces energy shortfall and PV power curtailment.
关键词： power system,Battery energy storage system (BESS),photovoltaic (PV) generation,PV generation forecast,unit commitment (UC)
更新于2025-09-23 15:22:29
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Assessing model performance of daily solar irradiance forecasts over Australia
摘要： In response to the rapid solar power installation worldwide, the solar industry is calling for more accurate solar irradiance forecasts with finer temporal and spatial granularity. It is timely to investigate if this need has been properly met by recent advancements in numerical weather prediction modelling. In this study, we validate and compare the current ability of three leading numerical weather prediction models to forecast daily solar irradiance for Australia. We found that all three models investigated perform well in the middle and west of Australia where clear sky weather prevails but struggle with forecasting solar irradiance in climatologically cloudy areas to some extent. In particular, the Global Forecast System (GFS) tends to significantly overpredict solar irradiance in southeastern Australia including Tasmania whilst the Australian Community Climate and Earth-System Simulator (ACCESS) system systematically underpredicts solar irradiance in northern Australia. The recent ERA5 reanalysis, which employs the Integrated Forecast System (IFS) to forecast solar irradiance, performs relatively robustly across Australia without notable deficiency, earning an overall forecast skill (defined as relative improvement in RMSE against 1-day persistence of clear-sky index) of 0.38 for 12-hour ahead forecasts of daily solar irradiance. An increase of the forecast skill to 0.44 is observed by linearly blending the three models.
关键词： Model blending,Solar forecasting,Numerical weather prediction,Forecast time horizon,Clear-sky index,Forecast skill
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Improvement of Global Horizontal Irradiance Forecasts from Unified Model over the Korean Peninsula by Using Model Output Statistics
摘要： Solar irradiance was forecasted by the operational numerical weather prediction model in Korea Meteorological Administration. The quantitative evaluation was made against the in situ measurements at 37 ground observing stations. The relative mean bias error values are ranging from -6.9% to 39.9% and then grouped by K-means clustering. For each cluster, the model output statistics are employed to correct the model biases, resulting in the reduction of mean absolute error for global horizontal irradiance forecasts.
关键词： model output statistics,error metrics,numerical weather prediction,solar irradiance forecast
更新于2025-09-23 15:19:57
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Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems
摘要： The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm.
关键词： water pumping system management,photovoltaic power,empirical models,forecast,artificial neural network,neuro fuzzy inference system
更新于2025-09-23 15:19:57
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Economic feasibility of solar power plants based on PV module with levelized cost analysis
摘要： In this study, the cost analysis of solar power system, where is located in Elaz??, Turkey is calculated according to levelized cost analysis method. In the economic feasibility studies carried out by the firms, many parameters such as interest rates, cost of money, detailed sunshine duration, monthly net profit-loss status in one year, cost of investment according to changing interest rates are not taken into consideration. All these parameters, which were not considered by the firms, have been calculated in this study. The payback period of investing in the solar power plant is calculated as 13 years, the payback period of it is calculated as an average of 6.6 years by the firms. The annual profit of a 1 MW solar energy plant is 89,467 US $. Present worth and annual capital cost of the solar power plant are calculated as 1,156,763 US $ and 1,181,875 US $, respectively. The capital cost flow of the investing in solar power plant is determined as 5.628 US $/h. When the results obtained from this study are evaluated in a general framework, the high interest rates in developing countries will have a negative effect on the investments in the solar power plants.
关键词： forecast solar radiation,Solar energy,photovoltaic,renewable energy,economic feasibility
更新于2025-09-19 17:15:36
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[IEEE 2019 International Conference on Power, Energy and Innovations (ICPEI) - Pattaya, Chonburi, Thailand (2019.10.16-2019.10.18)] 2019 International Conference on Power, Energy and Innovations (ICPEI) - Photovoltaic Power Generation Forecast by Using Estimator Model and Kalman Filter
摘要： This paper presents an approach to forecast photovoltaic (PV) power generation by using Kalman filter and Auto Regressive Integrated Moving Average (ARIMA). This method is suitable for real time forecast with high resolution time step and we use it to forecast for five-minute time step in this paper. However, Kalman filter requires real time measurement data to adjust forecast value, hence we propose an estimator model to help this approach to perform reliable forecast even when real time measurement data is unavailable. The dataset for building estimator model is set of historical data of power generation from neighbor PV rooftops and distance between PV rooftops. We use ARIMA model to estimate transition matrix for running Kalman filter. The performance of the test is measured by the Root Mean Square Error (RMSE) and Skill Score (SS). The obtained result shows that ARIMA model has lower accuracy compared to Kalman filter and estimator model. The real time data estimation from the estimator model can be used in Kalman filter to forecast PV power generation with good accuracy.
关键词： Photovoltaic forecast,Kalman Filter,Real time forecast,Auto Regressive Integrated Moving Average
更新于2025-09-16 10:30:52
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Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets
摘要： Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.
关键词： photovoltaic power forecast,GRA-LSTM model,historical power datasets,multivariate meteorological factors
更新于2025-09-16 10:30:52
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A Hybrid Probabilistic Estimation Method for Photovoltaic Power Generation Forecasting
摘要： Because of stochastic nature of weather conditions, the predictability of photovoltaic (PV) power generation is poor. Compared with the point prediction, the probabilistic prediction of PV power generation can provide more information about the underlying uncertainties, which is beneficial to the stability and safety of grid dispatching and power system. Based on random forest (RF), fuzzy C-means (FCM), sparse Gaussian process (SPGP), improved grey wolf optimizer (IMGWO) algorithm, a hybrid probabilistic estimation method, in this paper, is proposed to predict the probability of PV power generation for every hour in one day. RF algorithm is firstly used to reduce multidimensional input variables. And according to the weather patterns, FCM method is adopted to divide data and get the similar samples. Finally, a hybrid forecasting method combines SPGP and IMGWO is applied to forecast the test data. With the simulation and experimental results, the validity and reliability of the proposed model (IMGWOSP) is verified. The results show that the proposed model has improved both accuracy and practicability, so the stability and safety of grid dispatching and power system can be improved.
关键词： PV power forecast,Spare Gaussian process regression,Probability prediction,Improved grey wolf optimizer algorithm
更新于2025-09-12 10:27:22