scholarly journals Very Short-Term Nonparametric Probabilistic Forecasting of Renewable Energy Generation— With Application to Solar Energy

2016 ◽  
Vol 31 (5) ◽  
pp. 3850-3863 ◽  
Author(s):  
Faranak Golestaneh ◽  
Pierre Pinson ◽  
H. B. Gooi
2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Nousheen Fatima ◽  
Yanbin Li ◽  
Munir Ahmad ◽  
Gul Jabeen ◽  
Xiaoyu Li

Abstract Background The current research attempts to systematically investigate the causal interactions between renewable energy generation, aggregated energy use, human capital, and economic performance in Pakistan both in a short-term and long-term test for the period of 1990–2016. Methods As a primary step, a unit root analysis was conducted employing, among others, an augmented Dickey-Fuller-generalized least squares (ADF-GLS) test. Based on the order of integration I(1), the Johansen and Juselius (JJ) co-integration testing was employed to confirm a long-term causality analysis, which was followed by a vector error correction model (VECM) to calculate the short-run Granger causality analysis. Furthermore, the vector autoregressive (VAR)-based Cholesky test allowed the standard deviation impulse response functions to be generated to explain the responses of variables to arbitrary shocks in the data series under analysis. Results The empirical findings unearthed the bilateral causal connection between aggregated energy use and economic performance, renewable energy generation and economic performance, and human capital and economic performance. Thus, it confirmed the existence of feedback effects for aggregated energy use, renewable energy generation, and human capital in their relation to economic performance. Likewise, a unilateral positive causal connection was revealed running from renewable energy generation and human capital to aggregated energy use, and from human capital to renewable energy generation in both a long-term and short-term test. Additionally, the causal association running from aggregated energy use and renewable energy generation to economic performance was exposed in a long-term as well as short-term test, hence supporting the growth hypothesis. Conclusions The findings signified the importance of an enhanced generation of renewable energy along with the promotion of an aggregated energy use for the economic performance in Pakistan.


2020 ◽  
Vol 12 (4) ◽  
pp. 1653 ◽  
Author(s):  
Fatma Yaprakdal ◽  
M. Berkay Yılmaz ◽  
Mustafa Baysal ◽  
Amjad Anvari-Moghaddam

The inherent variability of large-scale renewable energy generation leads to significant difficulties in microgrid energy management. Likewise, the effects of human behaviors in response to the changes in electricity tariffs as well as seasons result in changes in electricity consumption. Thus, proper scheduling and planning of power system operations require accurate load demand and renewable energy generation estimation studies, especially for short-term periods (hour-ahead, day-ahead). The time-sequence variation in aggregated electrical load and bulk photovoltaic power output are considered in this study to promote the supply-demand balance in the short-term optimal operational scheduling framework of a reconfigurable microgrid by integrating the forecasting results. A bi-directional long short-term memory units based deep recurrent neural network model, DRNN Bi-LSTM, is designed to provide accurate aggregated electrical load demand and the bulk photovoltaic power generation forecasting results. The real-world data set is utilized to test the proposed forecasting model, and based on the results, the DRNN Bi-LSTM model performs better in comparison with other methods in the surveyed literature. Meanwhile, the optimal operational scheduling framework is studied by simultaneously making a day-ahead optimal reconfiguration plan and optimal dispatching of controllable distributed generation units which are considered as optimal operation solutions. A combined approach of basic and selective particle swarm optimization methods, PSO&SPSO, is utilized for that combinatorial, non-linear, non-deterministic polynomial-time-hard (NP-hard), complex optimization study by aiming minimization of the aggregated real power losses of the microgrid subject to diverse equality and inequality constraints. A reconfigurable microgrid test system that includes photovoltaic power and diesel distributed generators is used for the optimal operational scheduling framework. As a whole, this study contributes to the optimal operational scheduling of reconfigurable microgrid with electrical energy demand and renewable energy forecasting by way of the developed DRNN Bi-LSTM model. The results indicate that optimal operational scheduling of reconfigurable microgrid with deep learning assisted approach could not only reduce real power losses but also improve system in an economic way.


2021 ◽  
Vol 139 ◽  
pp. 110695
Author(s):  
KM Nazmul Islam ◽  
Tapan Sarker ◽  
Farhad Taghizadeh-Hesary ◽  
Anashuwa Chowdhury Atri ◽  
Mohammad Shafiul Alam

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