An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio

Solar radiation estimation with neural network approach using meteorological data in Indonesia

The objective of this study is to determine the solar energy potential in Indonesia using artificial neural networks (ANNs) approach. In this study, the meteorological data during 2005 to 2009 from 3 cities (Jakarta, Manado, Bengkulu) are used for training the neural networks and the data from 1 city (Makasar) is used for testing the estimated values.The testing data are not used in the training of the network in order to give an indication of the performance of the system at unknown locations. Fifteen combinations of ANN models were developed and evaluated.The multi layer perceptron ANNs model, with 7 inputs variables (average temperature, average relative humidity, average sunshine duration, longitude, latitude, latitude, month of the year) are proposed to estimate the global solar irradiation as output.To evaluate the performance of ANN models, statistical error analyses in terms of mean absolute percentage error (MAPE) are conducted for testing data. The best result of MAPE are found to be 7.4% when 7 neurons were set up in the hidden layer.The result demonstrates the capability of ANN approach to generate the solar radiation estimation in Indonesia using meteorological dat

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Updating the ionospheric propagation factor, M(3000)F2, global model using the neural network technique and relevant geophysical input parameters

This thesis presents an update to the ionospheric propagation factor, M(3000)F2, global empirical model developed by Oyeyemi et al. (2007) (NNO). An additional aim of this research was to produce the updated model in a form that could be used within the International Reference Ionosphere (IRI) global model without adding to the complexity of the IRI. M(3000)F2 is the highest frequency at which a radio signal can be received over a distance of 3000 km after reflection in the ionosphere. The study employed the artificial neural network (ANN) technique using relevant geophysical input parameters which are known to influence the M(3000)F2 parameter. Ionosonde data from 135 ionospheric stations globally, including a number of equatorial stations, were available for this work. M(3000)F2 hourly values from 1976 to 2008, spanning all periods of low and high solar activity were used for model development and verification. A preliminary investigation was first carried out using a relatively small dataset to determine the appropriate input parameters for global M(3000)F2 parameter modelling. Inputs representing diurnal variation, seasonal variation, solar variation, modified dip latitude, longitude and latitude were found to be the optimum parameters for modelling the diurnal and seasonal variations of the M(3000)F2 parameter both on a temporal and spatial basis. The outcome of the preliminary study was applied to the overall dataset to develop a comprehensive ANN M(3000)F2 model which displays a remarkable improvement over the NNO model as well as the IRI version. The model shows 7.11% and 3.85% improvement over the NNO model as well as 13.04% and 10.05% over the IRI M(3000)F2 model, around high and low solar activity periods respectively. A comparison of the diurnal structure of the ANN and the IRI predicted values reveal that the ANN model is more effective in representing the diurnal structure of the M(3000)F2 values than the IRI M(3000)F2 model. The capability of the ANN model in reproducing the seasonal variation pattern of the M(3000)F2 values at 00h00UT, 06h00UT, 12h00UT, and l8h00UT more appropriately than the IRI version is illustrated in this work. A significant result obtained in this study is the ability of the ANN model in improving the post-sunset predicted values of the M(3000)F2 parameter which is known to be problematic to the IRI M(3000)F2 model in the low-latitude and the equatorial regions. The final M(3000)F2 model provides for an improved equatorial prediction and a simplified input space that allows for easy incorporation into the IRI model

Prediction of Energy Consumption of an Administrative Building using Machine Learning and Statistical Methods

oai:ojs.pkp.sfu.ca:article/4099Energy management is now essential in light of the current energy issues, particularly in the building industry, which accounts for a sizable amount of global energy use. Predicting energy consumption is of great interest in developing an effective energy management strategy. This study aims to prove the outperformance of machine learning models over SARIMA models in predicting heating energy usage in an administrative building in Chefchaouen City, Morocco. It also highlights the effectiveness of SARIMA models in predicting energy with limited data size in the training phase. The prediction is carried out using machine learning (artificial neural networks, bagging trees, boosting trees, and support vector machines) and statistical methods (14 SARIMA models). To build the models, external temperature, internal temperature, solar radiation, and the factor of time are selected as model inputs. Building energy simulation is conducted in the TRNSYS environment to generate a database for the training and validation of the models. The models' performances are compared based on three statistical indicators: normalized root mean square error (nRMSE), mean average error (MAE), and correlation coefficient (R). The results show that all studied models have good accuracy, with a correlation coefficient of 0.90 < R < 0.97. The artificial neural network outperforms all other models (R=0.97, nRMSE=12.60%, MAE= 0.19 kWh). Although machine learning methods, in general terms, seemingly outperform statistical methods, it is worth noting that SARIMA models reached good prediction accuracy without requiring too much data in the training phase. Doi: 10.28991/CEJ-2023-09-05-01 Full Text: PD

Forecasting of Photovoltaic Power Production

Solar irradiance and temperature are some weather parameters that affect the amount of power photovoltaic cells can generate. Based on these and past power production, future production can be predicted. Knowing" future generation may help the integration of this renewable energy source on an even larger scale than today, as well as optimize the use of them today. In this thesis, forecasting of future power generation was made by an artificial neural network (ANN) model, a support vector regression (SVR) model, an auto-regressive integrated moving average (ARIMA) model, a quantile regression neural network (QRNN) model, an ensemble model of ANN and SVR, an ANN ensemble model and an ANN model using only numerical weather predictions (NWPs) as inputs. Correlation techniques and principal component analysis were used for feature reduction for all models. The research questions for this thesis are, "How will the models perform using random train data to predict August 2021, compared to a random test sample? Will the ensemble models perform better than the standalone models, and will the quantile regression neural network make accurate prediction intervals? How well will the predictions be if the ANN model only uses NWP data as inputs, compared to both historical power and NWPs?". As well as to answer these questions, the objective of this thesis is to provide a model or multiple models that can accurately predict future power production for the PV power system in Lillesand. All models can predict future power production, but some with less accuracy than others. Of all models, as expected, both ensemble models performed best overall for both tests. The SVR model did however perform with the lowest MAE for the August test. For different fits, these results will probably slightly change, but it is expected that the ensemble models will still perform best overall

Analysis of some meteorological parameters using artificial neural network method for Makurdi, Nigeria

The mean daily data for sunshine hours, maximum temperature, cloud cover and relative humidity data, were used to estimate monthly average global solar irradiation on a horizontal surface for Makurdi, Nigeria. The study used artificial neural networks (ANN) for the estimation. Results showed good agreement between the predicted and measured values of global solar irradiation. A correlation coefficient of 0.9982 was obtained with a maximum percentage error (MPE) of 0.8512 and root mean square error (RMSE) of 0.0032. The comparison between the ANN and some existing empirical models showed the advantage of the ANN prediction model.Key words: Sunshine hours, relative humidity, maximum temperature, cloudiness index, global solar radiation