scholarly journals ARMA model for short-term forecasting of solar potential: application to a horizontal surface of Dakar site

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
A. Mbaye ◽  
M.L. Ndiaye ◽  
D.M. Ndione ◽  
M. Sylla ◽  
M.C. Aidara ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Horizontal Surface ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
P Value ◽  
Short Term ◽  
Arma Process ◽  
Short Term Forecasting ◽  
Sahelian Zone

This paper presents a model for short-term forecasting of solar potential on a horizontal surface. This study is carried out in to the context of valuing of energy production from photovoltaic solar sources in the Sahelian zone. In this study, Autoregressive Moving Average (ARMA) process is applied to predict global solar potential upon 24 hours ahead. The ARMA (p, q) is based on finding optimum parameters p and q to better fit considered variable (sunshine). Data used for the model calibrating are measured at the station of Ecole Supérieure Polytechnique of Dakar. Records are hourly and range from October 2016 to September 2017. The choice of this model is justified by its robustness and its applicability on several scales through the world. Simulation is done using the RStudio software. The Akaike information criterion shows that ARMA (29, 0) gives the best representation of the data. We then applied a white noise test to validate the process. It confirms that the noise is of white type with zero mean, variance of 1.252 and P-value of about 26% for a significant level of 5%.Verification of the model is doneby analyzing some statistical performance criteria such the RMSE =0.629 (root mean squared error), the R² = 0.963 (Coefficient of determination), the MAE=0.528 (Mean Absolut Error) and the MBE=0.012 (Mean BiasError). Statistics criteria show that the ARMA (29,0) is reliable; then, can help to improve planning of photovoltaic solar power plants production in the Sahelian zone.

scholarly journals A Comparison of Different Short-Term Macroeconomic Forecasting Models: Evidence from Armenia

2016 ◽  
Vol 5 (2) ◽  
pp. 81-99 ◽  
Author(s):  
Karen Poghosyan
Keyword(s):  
Mean Squared Error ◽  
Small Scale ◽  
Var Models ◽  
Short Term ◽  
Macroeconomic Forecasting ◽  
Bayesian Var ◽  
Out Of Sample ◽  
Competing Models ◽  
Bayesian Factor ◽  
Short Term Forecasting

Abstract We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE) criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than traditional unrestricted VAR model.

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

scholarly journals Evaluation of Random Forest for short-term daily streamflow forecast in rainfall and snowmelt driven watersheds

2020 ◽  
Author(s):  
Leo T. Pham ◽  
Lifeng Luo ◽  
Andrew O. Finley
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Climatic Conditions ◽  
Coefficient Of Determination ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Streamflow Forecast ◽  
Daily Streamflow ◽  
Rf Performance

Abstract. In the past decades, data-driven Machine Learning (ML) models have emerged as promising tools for short-term streamflow forecasts. Among other qualities, the popularity of ML for such applications is due to the methods' competitive performance compared with alternative approaches, ease of application, and relative lack of strict distributional assumptions. Despite the encouraging results, most applications of ML for streamflow forecast have been limited to watersheds where rainfall is the major source of runoff. In this study, we evaluate the potential of Random Forest (RF), a popular ML method, to make streamflow forecast at 1-day lead time at 86 watersheds in the Pacific Northwest. These watersheds span climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes: rainfall-dominated, transisent, and snowmelt-dominated based on the timing of center of annual flow volume. RF performance is benchmarked against Naive and multiple linear regression (MLR) models, and evaluated using four metrics Coefficient of determination, Root mean squared error, Mean absolute error, and Kling-Gupta efficiency. Model evaluation metrics suggest RF performs better in snowmelt-driven watersheds. Largest improvement in forecasts, compared to benchmark models, are found among rainfall-driven watersheds. We obtain Kling–Gupta Efficiency (KGE) scores in the range of 0.62–0.99. RF performance deteriorates with increase in catchment slope and increase in soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.

Short-Term Forecasting of Uncertain Parameters for Virtual Power Plants

2021 ◽  
Author(s):  
Mahdi Abbasi ◽  
Amirabbas Asadi ◽  
Seifeddine BenElghali ◽  
Mohamed Zerrougui
Keyword(s):  
Power Plants ◽  
Uncertain Parameters ◽  
Virtual Power ◽  
Short Term ◽  
Virtual Power Plants ◽  
Short Term Forecasting

scholarly journals Solar Power Plant Generation Short-Term Forecasting Model

2018 ◽  
Vol 208 ◽  
pp. 04004
Author(s):  
Stanislav Eroshenko ◽  
Elena Kochneva ◽  
Pavel Kruchkov ◽  
Aleksandra Khalyasmaa
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Solar Power ◽  
Weather Conditions ◽  
Solar Power Plant ◽  
Short Term ◽  
Variable Capacity ◽  
Solar Power Plants ◽  
Rapid Pace ◽  
Short Term Forecasting

Recently, renewable generation plays an increasingly important role in the energy balance. Solar energy is developing at a rapid pace, while the solar power plants output depends on weather conditions. Solar power plant output short-term forecasting is an urgent issue. The future electricity generation qualitative forecasts allow electricity producers and network operators to actively manage the variable capacity of solar power plants, and thereby to optimally integrate the solar resources into the country's overall power system. The article presents one of the possible approaches to the solution of the short-term forecasting problem of a solar power plant output.

scholarly journals Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds

2021 ◽  
Vol 25 (6) ◽  
pp. 2997-3015
Author(s):  
Leo Triet Pham ◽  
Lifeng Luo ◽  
Andrew Finley
Keyword(s):  
Random Forests ◽  
Mean Squared Error ◽  
Predictive Performance ◽  
Absolute Error ◽  
Climatic Conditions ◽  
Coefficient Of Determination ◽  
Streamflow Forecasting ◽  
Short Term ◽  
Daily Streamflow ◽  
Rf Performance

Abstract. In the past decades, data-driven machine-learning (ML) models have emerged as promising tools for short-term streamflow forecasting. Among other qualities, the popularity of ML models for such applications is due to their relative ease in implementation, less strict distributional assumption, and competitive computational and predictive performance. Despite the encouraging results, most applications of ML for streamflow forecasting have been limited to watersheds in which rainfall is the major source of runoff. In this study, we evaluate the potential of random forests (RFs), a popular ML method, to make streamflow forecasts at 1 d of lead time at 86 watersheds in the Pacific Northwest. These watersheds cover diverse climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes based on the timing of center-of-annual flow volume: rainfall-dominated, transient, and snowmelt-dominated. RF performance is benchmarked against naïve and multiple linear regression (MLR) models and evaluated using four criteria: coefficient of determination, root mean squared error, mean absolute error, and Kling–Gupta efficiency (KGE). Model evaluation scores suggest that the RF performs better in snowmelt-driven watersheds compared to rainfall-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.

scholarly journals Kalman filter model, as a tool for short-term forecasting of solar potential: case of the Dakar site

2018 ◽  
Vol 57 ◽  
pp. 01004
Author(s):  
A. Mbaye ◽  
J. Ndong ◽  
M.L. NDiaye ◽  
M. Sylla ◽  
M.C. Aidara ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Mean Squared Error ◽  
Model Performance ◽  
Global Solar Radiation ◽  
Plant Production ◽  
Bias Error ◽  
Short Term ◽  
Filter Model ◽  
Short Term Forecasting

The prediction of solar potential is an important step toward the evaluation of PV plant production for the best energy planning. In this study, the discrete Kalman filter model was implemented for short-term solar resource forecasting one the Dakar site in Senegal. The model input parameters are constituted at a time t of the air temperature, the relative humidity and the global solar radiation. The expected output at time t+T is the global solar radiation. The model performance is evaluated with the square root of the normalized mean squared error (NRMSE), the absolute mean of the normalized error (NMAE), the average bias error (NMBE). The model Validation is carried out by means of the data measured within the Polytechnic Higher School of Dakar for one year. The simulation results following the 20 minute horizon show a good correlation between the prediction and the measurement with an NRMSE of 4.8%, an NMAE of 0.27% and an NMBE of 0.04%. This model could contribute to help photovoltaic based energy providers to better plan the production of solar photovoltaic plants in Sahelian environments.

scholarly journals Solar power generation short-term forecasting model’s implementation experience

2018 ◽  
Vol 208 ◽  
pp. 04005 ◽  
Author(s):  
Elena Kochneva
Keyword(s):  
Electricity Market ◽  
Power Plants ◽  
Solar Power ◽  
Information Support ◽  
Practical Implementation ◽  
Short Term ◽  
Forecasting System ◽  
Solar Power Plants ◽  
Short Term Forecasting ◽  
Installed Capacity

Recently there is significant increase in the installed capacity of solar power plants in Russia. Thereby there are issues of solar power plants owners information support for participation in wholesale electricity market. The paper describes the experience of short term forecasting system practical implementation. The system is proposed for forecasting the solar power plant generation “a day ahead” as a part of the software for automatic meter reading systems “Energosfera”. The short-term forecasting program modules structure, key parameters and characteristics used during the forecasting process description is presented.

Short-term forecasting model for electric power production of small-hydro power plants

2013 ◽  
Vol 50 ◽  
pp. 387-394 ◽  
Author(s):  
Claudio Monteiro ◽  
Ignacio J. Ramirez-Rosado ◽  
L. Alfredo Fernandez-Jimenez
Keyword(s):  
Electric Power ◽  
Power Plants ◽  
Power Production ◽  
Forecasting Model ◽  
Short Term ◽  
Hydro Power ◽  
Electric Power Production ◽  
Hydro Power Plants ◽  
Short Term Forecasting

scholarly journals Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region

2017 ◽  
Vol 113 (11/12) ◽  
Author(s):  
Zinhle Mashaba ◽  
George Chirima ◽  
Joel O. Botai ◽  
Ludwig Combrinck ◽  
Cilence Munghemezulu ◽  
...  
Keyword(s):  
Linear Regression ◽  
Model Validation ◽  
Growth Stage ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Wheat Yield ◽  
Free State ◽  
Coefficient Of Determination ◽  
P Value ◽  
State Region

Consumption of wheat is widespread and increasing in South Africa. However, global wheat production is projected to decline. Wheat yield forecasting is therefore crucial for ensuring food security for the country. The objective of this study was to investigate whether the anthesis wheat growth stage is suitable for forecasting dryland wheat yields in the Central Free State region using satellite imagery and linear predictive modelling. A period of 10 years of Normalized Difference Vegetation Index data smoothed with a Savitzky–Golay filter and 10 years of wheat yield data were used for model calibration. Diagnostic plots and statistical procedures were used for model validation and assessment of model adequacy. The period 30 days before harvest during the anthesis stage was established to be the best period during which to use the linear regression model. The calibrated model had a coefficient of determination of 0.73, a p-value of 0.00161 and a root mean squared error of 0.41 tons/ha. Residual plots confirmed that a linear model had a good fit for the data. The quantile-quantile plot provided evidence that the residuals were normally distributed, which means that assumptions of linear regression were fulfilled and the model can be used as a forecasting tool. Model validation showed high levels of accuracy. The evidence indicates that use of Moderate Resolution Imaging Spectroradiometer data during the anthesis growth stage is a reliable, cost-effective and potentially time-saving alternative to ground-based surveys when forecasting dryland wheat yields in the Central Free State.

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