Browsing by Author "Dhakal, Rabin"
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Item Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production(2023) Sedai, Ashish (TTU); Dhakal, Rabin; Gautam, Shishir; Dhamala, Anibesh (TTU); Bilbao, Argenis (TTU); Wang, Qin; Wigington, Adam; Pol, Suhas (TTU)The Machine Learning/Deep Learning (ML/DL) forecasting model has helped stakeholders overcome uncertainties associated with renewable energy resources and time planning for probable near-term power fluctuations. Nevertheless, the effectiveness of long-term forecasting of renewable energy resources using an existing ML/DL model is still debatable and needs additional research. Considering the constraints inherent in current empirical or physical-based forecasting models, the study utilizes ML/DL models to provide long-term predictions for solar power production. This study aims to examine the efficacy of several existing forecasting models. The study suggests approaches to enhance the accuracy of long-term forecasting of solar power generation for a case study power plant. It summarizes and compares the statistical model (ARIMA), ML model (SVR), DL models (LSTM, GRU, etc.), and ensemble models (RF, hybrid) with respect to long-term prediction. The performances of the univariate and multivariate models are summarized and compared based on their ability to accurately predict solar power generation for the next 1, 3, 5, and 15 days for a 100-kW solar power plant in Lubbock, TX, USA. Conclusions are drawn predicting the accuracy of various model changes with variation in the prediction time frame and input variables. In summary, the Random Forest model predicted long-term solar power generation with 50% better accuracy over the univariate statistical model and 10% better accuracy over multivariate ML/DL models.Item Renewable energy resource assessment for rural electrification: a case study in Nepal(2023) Sedai, Ashish (TTU); Dhakal, Rabin; Koirala, Pranik; Gautam, Shishir; Pokhrel, Rajat; Lohani, Sunil Prasad; Moussa, Hanna (TTU); Pol, Suhas (TTU)Renewable energy could mitigate remote area energy crises through rural electrification. Karnali province, one of the seven federal provinces of Nepal, is such a remote location and is most deprived in terms of electricity access. Around 67% of the population of the Karnali province is not connected to the national grid electricity supply. High altitude, mountainous topography makes it difficult to provide grid access to the region. This study summarizes the current electricity access status in Nepal and Karnali province specifically. The paper discusses the energy, economic and environmental (3E) analysis of different renewable energy resources like solar and wind energy for the grid-isolated region in Mugu and Jumla district of Karnali province. The study investigates the feasibility of a 200-kW solar power plant installation in Gamghadi, the capital of Mugu district and a 100-kW wind power plant installation in Tila village, Jumla district. The study suggests whether a similar installation of the distributed energy plant is a solution to mitigate the energy crisis problem in the high Himalayas regions, like Karnali province of Nepal. Based on the high-level resource assessment, the study estimates an investment cost ranging from 7 to 9 million USD would be necessary for the installation of such distributed solar PV and wind turbines.Item Wind Speed Prediction using Classical Time Series and Machine Learning Models: A Comparative Analysis(2022-12) Dhakal, Rabin; Moussa, Hanna; Parameswaran, Siva; Maldonado, Victor; Pol, Suhas; Nejat, AliThe need of delivering future accurate predictions of renewable energy generation has been recognized by stakeholders working in the field of renewable energy. It is the reason for recent improvements in the methods to provide more precise energy generation prediction. Wind power production is dependent on weather pattern variations, particularly wind speed, which are irregular in locations with unpredictable weather. Wind speed prediction in a given location is crucial for the evaluation of the wind power project; the accurate prediction improves the planning, reduces the cost, and improves the use of resources for wind power generation. Models such as Weibull probability density function based wind prediction (WBM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid methods classical time series and deep learning models have been used for accurate prediction of wind speed with different forecast horizons. For short and ultra-short terms that are two to three hours in the future, the ARIMA ensemble with ANN has demonstrated improved performance. For medium-term wind speed predictions, however, SVR, Kalman filters, and ensembles of both have demonstrated good performance. Recurrent neural networks (RNN) in particular have recently reported enormous success in time series forecasts, especially for medium- and long-term predictions. There has been growing interest in the field of deep learning and neural networks for the prediction of wind speed as it can overcome the issue of accurately forecasting the nonlinear patterns of wind speed data using classical time series methods. The main contribution of this dissertation research is the comparative analysis of the performance of each method for accurately predicting wind speed for different time horizons and proposing a Weibull distribution based featured engineered hybrid model for wind speed prediction. In this research, the wind speed generated from the Weibull probability density function is used as a feature in the wind speed prediction model and the prediction model is developed by optimizing the weight function for each model contributed to the hybrid prediction model. The demonstration of the accuracy of the 7 proposed model and comparative analysis of the different model is performed on the five different data set obtained from the National Oceanic and Atmospheric Administration and System Advisory Module (SAM) database.