scholarly journals Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

2020 ◽  
Vol 13 (2) ◽  
pp. 188-195
Author(s):  
Kriangkamon Khumma ◽  
Kreangsak Tamee
Keyword(s):  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Minute Period ◽  
Filtering Technique ◽  
Stochastic Forecasting ◽  
Short Term Forecasting ◽  
Persistence Model ◽  
Power Forecasting

    This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm filtering technique to estimate power output and the creation of a stochastic forecasting model. As a result, affected power can be forecasted from stochastic factors with machine learning and an artificial neural network. This model focuses on very short-term forecasting over a five minute period. As it uses only endogenous data, no exogenous data is needed.      To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.

scholarly journals Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 843 ◽  
Author(s):  
Keke Wang ◽  
Dongxiao Niu ◽  
Lijie Sun ◽  
Hao Zhen ◽  
Jian Liu ◽  
...  
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Harmony Search ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Correlation Degree ◽  
Learning Machine ◽  
Power Forecasting

Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The CEEMD-SE-HS-KELM forecasting model constructed in this paper is used in the short-term wind power forecasting of a Chinese wind farm, and the RMSE and MAE are as 2.16 and 0.39 respectively, which is better than EMD-SE-HS-KELM, HS-KELM, KELM and extreme learning machine (ELM) model. According to the experimental results, the hybrid method has higher forecasting accuracy for short-term wind power forecasting.

scholarly journals The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation

2020 ◽  
Vol 10 (22) ◽  
pp. 8265
Author(s):  
Stanislav A. Eroshenko ◽  
Alexandra I. Khalyasmaa ◽  
Denis A. Snegirev ◽  
Valeria V. Dubailova ◽  
Alexey M. Romanov ◽  
...  
Keyword(s):  
Power Generation ◽  
Time Domain ◽  
Power Plants ◽  
Multiple Time ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Operational Forecasting ◽  
Short Term Forecasting ◽  
Forecasting Errors

The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.

Short-term forecasting model for aggregated regional hydropower generation

2014 ◽  
Vol 88 ◽  
pp. 231-238 ◽  
Author(s):  
Claudio Monteiro ◽  
Ignacio J. Ramirez-Rosado ◽  
L. Alfredo Fernandez-Jimenez
Keyword(s):  
Forecasting Model ◽  
Hydropower Generation ◽  
Short Term ◽  
Short Term Forecasting

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

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