A review on the selected applications of forecasting models in renewable power systems

Ahmed, Adil; Khalid, Muhammad

doi:10.1016/j.rser.2018.09.046

Cited by 226 publications

(74 citation statements)

References 158 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have developed different methods for solar irradiance forecasting, such as statistical approaches using historical data, use of numerical weather prediction (NWP) models, tracking cloud movements from satellite images and tracking cloud movements from direct ground observations using sky cameras [8]. Some reviews on renewable energy forecasting models are given in References [6][7][8][9][10][11] among others. Some notable methods are discussed in the following paragraphs.…”

Section: An Overview Of the Literature On Solar Irradiance Forecastingmentioning

confidence: 99%

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

et al. 2019

View full text Add to dashboard Cite

Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.

show abstract

Section: An Overview Of the Literature On Solar Irradiance Forecastingmentioning

confidence: 99%

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The three methods [4,7,9] are FTS models with simple and easy understanding, but they do not consider more key factors, hence their performance are not better than the proposed model. Furthermore, GRNN has local minimal point and over-fitting problems, and the computational requirement of the SVR is quite tedious [53], especially how to select the fitting kernel function is a key question.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…A simple and easy to explain model is the motivation of this study; therefore, we used three factors to build a linear combination of multifactor fuzzy time-series for forecasting stock index. From Ahmed and Khalid [53], we found the ANN-based forecasting models have some drawbacks such as local minimal point, over-fitting problems, etc. These drawbacks can be overcome by advanced hybrid intelligent models like SVM, ELM, and ANFIS.…”

Section: Discussionmentioning

confidence: 99%

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

2019

View full text Add to dashboard Cite

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

show abstract

“…In (1)(2)(3)(4)(5), the variables represent the following meanings: = Forget gate, = Input gate, = Output gate, ′ = Intermediate cell state, and = Cell state. These are the equations of gates and cell states of the LSTM.…”

Section: Casual Structure Of Proposed Rf-bi-lstm Hybrid Modelmentioning

confidence: 99%

“…Short term load forecasting (STLF) is a crucial instrument for ensuring the supply of high-quality electricity to customers in a systematic economic manner. Forecasts of electricity serve as the basis of many operating decisions taken by the electric utilities such as resource acquisition, dispatch scheduling of generating capacity, plant maintenance schedule, reliability analysis, and efficient power plant design [1]. The necessity of reliable load forecasting is more significant than ever as most of the energy markets around the world are becoming deregulated [2].…”

Section: Introductionmentioning

confidence: 99%

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

Ferdoush

Mahmud

Chakrabarty

et al. 2021

IJECE

View full text Add to dashboard Cite

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.

show abstract

A review on the selected applications of forecasting models in renewable power systems

Cited by 226 publications

References 158 publications

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

Contact Info

Product

Resources

About