A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

Jadidi, Aydin; Menezes, Raimundo; Souza, Nilmar de; Lima, Carla Caroline Valença de

doi:10.3390/en11102641

Cited by 26 publications

(10 citation statements)

References 22 publications

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“…It is often used as a comparison with other advanced methods [39]. In the irradiance forecasting community, numerous works have been devoted recently to the development of models that generate deterministic or point forecasts [34,[40][41][42][43][44][45]. In this work, as we are dealing with short-term forecasting of solar irradiance, we have considered the persistence model and smart persistence model for comparison purposes.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Forecasting Applied to Power Systems

Bracale¹,

Falco²

2020

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Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level, from transmission to distribution networks, and in the new context of smart grids. Many important operations are scheduled and performed on the basis of predictions of several energy-related variables, such as non-controllable generation, loads, energy prices, and power quality indicators. Forecasts of these variables at different lead times, ranging from several minutes to several days, are needed by the operators in order to suit different applications and scenarios. The application of forecasting techniques to power systems, in both deterministic and probabilistic frameworks, is a topic that is still far from being fully explored. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have successfully been applied within several frameworks, and have recently started to attract the interest of power system practitioners. The subject is therefore worthy of further investigation. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energyrelated variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems. This book is relevant for forecasters and energy practitioners involved in power system planning and management. It also represents a good starting point for young researchers who want to discover potentialities of ensemble forecasting systems. Eventually, the contents of the book may be used by system operators and market operators in order to boost the performance of their inhouse forecasting systems through ensemble refinement.

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Section: Introductionmentioning

confidence: 99%

Ensemble Forecasting Applied to Power Systems

Bracale¹,

Falco²

2020

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Section: Introductionmentioning

confidence: 99%

Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

2019

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Background: The Distributed Energy Resources (DERs) are beneficial in reducing the electricity bills of the end customers in a smart community by enabling them to generate electricity for their own use. In the past, various studies have shown that owing to a lack of awareness and connectivity, end customers cannot fully exploit the benefits of DERs. However, with the tremendous progress in communication technologies, the Internet of Things (IoT), Big Data (BD), machine learning, and deep learning, the potential benefits of DERs can be fully achieved, although a significant issue in forecasting the generated renewable energy is the intermittent nature of these energy resources. The machine learning and deep learning models can be trained using BD gathered over a long period of time to solve this problem. The trained models can be used to predict the generated energy through green energy resources by accurately forecasting the wind speed and solar irradiance. Methods: We propose an efficient approach for microgrid-level energy management in a smart community based on the integration of DERs and the forecasting wind speed and solar irradiance using a deep learning model. A smart community that consists of several smart homes and a microgrid is considered. In addition to the possibility of obtaining energy from the main grid, the microgrid is equipped with DERs in the form of wind turbines and photovoltaic (PV) cells. In this work, we consider several machine learning models as well as persistence and smart persistence models for forecasting of the short-term wind speed and solar irradiance. We then choose the best model as a baseline and compare its performance with our proposed multiheaded convolutional neural network model. Results: Using the data of San Francisco, New York, and Los Vegas from the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory (NREL) as a case study, the results show that our proposed model performed significantly better than the baseline model in forecasting the wind speed and solar irradiance. The results show that for the wind speed prediction, we obtained 44.94%, 46.12%, and 2.25% error reductions in root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), respectively. In the case of solar irradiance prediction, we obtained 7.68%, 54.29%, and 0.14% error reductions in RMSE, mean bias error (MBE), and sMAPE, respectively. We evaluate the effectiveness of the proposed model on different time horizons and different climates. The results indicate that for wind speed forecast, different climates do not have a significant impact on the performance of the proposed model. However, for solar irradiance forecast, we obtained different error reductions for different climates. This discrepancy is certainly due to the cloud formation processes, which are very different for different sites with different climates. Moreover, a detailed analysis of the generation estimation and electricity bill reduction indicates that the proposed framework will help the smart community to achieve an annual reduction of up to 38% in electricity bills by integrating DERs into the microgrid. Conclusions: The simulation results indicate that our proposed framework is appropriate for approximating the energy generated through DERs and for reducing the electricity bills of a smart community. The proposed framework is not only suitable for different time horizons (up to 4 h ahead) but for different climates.

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“…Diverse techniques have been applied in demand forecasting problems such as techniques based on time series and regression analysis [3][4][5]. However, because of the non-linear nature of the problem, techniques based on artificial neural networks and Adaptive Neuro-Fuzzy Inference System (ANFIS) are more popular [6][7][8][9][10][11]. As an example, Barak and Sadegh [12] proposed a hybrid ARIMA-ANFIS model for forecasting of the annual energy consumption of Iran.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

et al. 2019

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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.

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A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

Cited by 26 publications

References 22 publications

Ensemble Forecasting Applied to Power Systems

Ensemble Forecasting Applied to Power Systems

Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

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