Abstract
With the increasing proportion of solar energy in the energy system, accurate solar irradiance forecast is of great significance for low-cost energy scheduling. This paper proposes a new forecasting idea for day-ahead solar irradiance forecast on the day-ahead scale: Firstly, based on formula derivation and big data correlation analysis, this paper finds out multiple parameters related to GHI, and jointly uses these parameters to forecast GHI. Error revision during morning period (ERDMP) is innovatively proposed on this basis, towards a more accurate daytime forecast. In order to prove the reliability and universality of the method, relevant data from five different-climatic regions are respectively used in the experiment. The multi-variable historical data-based day-ahead solar irradiance forecast uses deep neural networks, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network. ERDMP uses a linear AutoRegressive (AR) model to predict the daytime error coefficients based on the morning error coefficients. According to the results, through the proposed ERDMP, Mean Absolute Error (MAE) decreases by about 25% to 30%, Root Mean Squared Error (RMSE) decreases by about 20%, and R2 increases by about 5% to 10% when compared with the initial error of multi-parameter prediction models and other advanced models.
Similar content being viewed by others
Data availability
If data and materials are needed, contact the email (darenyu2023@163.com) at any time, please. I will reply to you as soon as possible.
References
Aggarwal S, Saini L (2014) Solar energy prediction using linear and non-linear regularization models: a study on AMS (American Meteorological Society) 2013–14 solar energy prediction contest. Energy 78:247–256
Ahmad A, Anderson T et al (2015) Hourly global solar irradiation forecasting for New Zealand. Sol Energy 122:1398–1408
Ali A, Ahmed A et al (2022) Short-Term Load Forecasting Based on CNN and LSTM Deep Neural Networks. IFAC-PapersOnLine 55:777–781
Ashutosh K, Abhishek K et al (2021) Study and analysis of SARIMA and LSTM in forecasting time series data. Sustain Energy Technol Assess 47:2213–1388
Aslam M, Seung KH et al (2019) Long-term solar radiation forecasting using a deep learning approach-GRUs. 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP), 917–920
Bai M, Chen Y et al (2022) Deep attention ConvLSTM-based adaptive fusion of clear-sky physical prior knowledge and multivariable historical information for probabilistic prediction of photovoltaic power. Expert Syst Appl 202:117335
Bergman J, Salby M (1996) Diurnal variations of cloud cover and their relationship to climatological conditions. J Clim 9(11):2802–2820
Brester C, Kallio-Myers V et al (2023) Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations. Renew Energy 207:266–274
Cao J, Cao S (2006) Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 31:3435–3445
Chandola D, Gupta H et al (2020) Multi-step ahead forecasting of global solar radiation for arid zones using deep learning. Procedia Comput Sci 167:626–635
David M, Aguiar Luis M et al (2018) Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data. Int J Forecast 34:529–547
Demirhan H, Renwick Z (2018) Missing value imputation for short to mid-term horizontal solar irradiance data. Appl Energy 225:998–1012
Dulakshi SKK et al (2022) Root mean square error or mean absolute error? Use their ratio as well. Inf Sci 585:609–629
Haider S, Sajid M et al (2022) Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad. Renew Energy 198:51–60
Jaihuni M, Basak J et al (2022) A novel recurrent neural network approach in forecasting short term solar irradiance. ISA Trans 121:63–74
Jihye M, Md B et al (2021) AR and ARMA model order selection for time-series modeling with ImageNet classification. Signal Process 183:108026
Ju Y, Sun G (2019) A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access 7:28309–28318
Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and arima models for time series forecasting. Appl Soft Comput 11:2664–2675
Kumari P, Toshniwal D (2021) Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting. Appl Energy 295:117061
Lang P, Li G et al (2022) Forecasting Research on Long-term Solar Irradiance with An Improved Prophet Algorithm. IFAC-PapersOnLine 9:491–494
Li P, Ng J et al (2022) Accelerating the adoption of renewable energy certificate: Insights from a survey of corporate renewable procurement in Singapore. Renew Energy 199:1272–1282
Liu C, Gu J et al (2021) A simplified LSTM neural networks for one day-ahead solar power forecasting. IEEE Access 9:17174–17195
Liu Y, Qian Y et al (2022) Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar. Sol Energy 241:1–12
Liu J, Zang H et al (2023) A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting. Appl Energy 342:121160
Mai S, Ayman W et al (2022) Semi-supervised deep learning framework for milk analysis using NIR spectrometers. Chemom Intell Lab Syst 228:104619
Mayer MJ (2022) Benefits of physical and machine learning hybridization for photovoltaic power forecasting. Renew Sustain Energy Rev 168:112772
Mejia J, Giordano M et al (2018) Conditional summertime day-ahead solar irradiance forecast. Sol Energy 163:610–622
Mir MAM, Seyedeh Y et al (2022) A prediction of future flows of ephemeral rivers by using stochastic modeling (AR autoregressive modeling). Sustain Oper Comput 3:330–335
Miranda E, Fierro JFG et al (2021) Prediction of site-specific solar diffuse horizontal irradiance from two input variables in Colombia. Heliyon 7(12):e08602
Namini SS, Tavakoli N et al (2018) A Comparison of ARIMA and LSTM in Forecasting Time Series. IEEE 293:11633
Paszkuta M, Zapadka T et al (2022) Diurnal variation of cloud cover over the Baltic Sea. Oceanologia 64(2):299–311
Perez R, Ineichen P et al (1990) Modeling daylight availability and irradiance components from direct and global irradiance. Sol Energy 44:271–289
Qazi S (2017) Standalone Photovoltaic (PV) Systems for Disaster Relief and Remote Areas. Elsevier, 203–237
Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468
Ramadhan R, Heatubun Y et al (2021) Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power. Renew Energy 178:1006–1019
Reno M, Hansen C (2016) Identification of periods of clear sky irradiance in time series of GHI measurements. Renew Energy 90:520–531
Schulz B, Ayari M et al (2021) Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting. Sol Energy 220:1016–1031
Spacagna G, Slater D et al (2019) Computer vision with convolutional networks. Dhandre P, Deokar Y, Dias N, Shingote K, Safis (Eds.). Python Deep Learning, Packt Publishing, Birmingham. 93–121
Sward JA, Ault TR et al (2022) Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration. Energy 254:124367
Verbois H, Rusydi A et al (2018) Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting. Sol Energy 73:313–327
Wang W, Chau K et al (2015) Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition. Environ Res 139:46–54
Wang K, Qi X et al (2019) A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Appl Energy 251:113315
Wang W, Yang D et al (2022) An archived dataset from the ECMWF Ensemble Prediction System for probabilistic solar power forecasting. Sol Energy 248:217–225
Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 30(1):79–82
Xie Y, Yang J et al (2022) Improving the prediction of DNI with physics-based representation of all-sky circumsolar radiation. Sol Energy 231:758–766
Yan P, Zhang Z, Hou Q, Lei X, Liu Y, Wang H (2023) A novel IBAS-ELM model for prediction of water levels in front of pumping stations. J Hydrol 616:128810
Yand D, Panida J, Wilfred M (2012) Hourly solar irradiance time series forecasting using cloud cover index. Sol Energy 86:3531–3543
Yang D (2018) A correct validation of the National Solar Radiation Data Base (NSRDB). Renew Sustain Energy Rev 97:152–155
Yang D (2022) Correlogram, predictability error growth, and bounds of mean square error of solar irradiance forecasts. Renew Sustain Energy Rev 167:112736
Yang H, Yan J et al (2022a) Statistical downscaling of numerical weather prediction based on convolutional neural networks. Global Energy Interconnection 5:64–75
Yang Y, Sun W et al (2022b) Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data. Remote Sens Environ 273:112971
Zhang GP (2003) Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50:159–175
Author information
Authors and Affiliations
Contributions
Yunxiao Chen is responsible for the experiment process and article writing.
Mingliang Bai is responsible for data collection and experimental guidance.
Yilan Zhang is responsible for literature research.
Jinfu Liu is responsible for guiding the article format.
Daren Yu is responsible for the guidance of methods and ideas.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests.
Additional information
Communicated by: H. Babaie
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, Y., Bai, M., Zhang, Y. et al. Error revision during morning period for deep learning and multi-variable historical data-based day-ahead solar irradiance forecast: towards a more accurate daytime forecast. Earth Sci Inform 16, 2261–2283 (2023). https://doi.org/10.1007/s12145-023-01026-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-01026-3