scholarly journals Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Mostafa Majidpour ◽  
Hamidreza Nazaripouya ◽  
Peter Chu ◽  
Hemanshu Pota ◽  
Rajit Gadh
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Solar Power ◽  
Time Series Prediction ◽  
Percentage Error ◽  
Acquisition Time ◽  
Support Vector ◽  
Solar Power Generation ◽  
Prediction Algorithms ◽  
Univariate Time Series

In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.

scholarly journals Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning

2021 ◽  
Vol 5 (1) ◽  
pp. 50
Author(s):  
Phil Aupke ◽  
Andreas Kassler ◽  
Andreas Theocharis ◽  
Magnus Nilsson ◽  
Michael Uelschen
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Time Series ◽  
Renewable Energy ◽  
Solar Power ◽  
Time Series Data ◽  
Series Data ◽  
Solar Power Generation ◽  
Renewable Energy Generation ◽  
Micro Grids

Recently, there has been growing interest in using machine learning based methods for forecasting renewable energy generation using time-series prediction. Such forecasting is important in order to optimize energy management systems in future micro-grids that will integrate a large amount of solar power generation. However, predicting solar power generation is difficult due to the uncertainty of the solar irradiance and weather phenomena. In this paper, we quantify the impact of uncertainty of machine learning based time-series predictors on the forecast accuracy of renewable energy generation using long-term time series data available from a real micro-grid in Sweden. We use clustering to build different ML forecasting models using LSTM and Facebook Prophet. We evaluate the accuracy impact of using interpolated weather and radiance information on both clustered and non-clustered models. Our evaluations show that clustering decreases the uncertainty by more than 50%. When using actual on-side weather information for the model training and interpolated data for the inference, the improvements in accuracy due to clustering are the highest, which makes our approach an interesting candidate for usage in real micro-grids.

Displacement Time Series Prediction Model of Landslide Based on Phase Space Reconstruction

2014 ◽  
Vol 1065-1069 ◽  
pp. 23-26
Author(s):  
Ting Yao Jiang ◽  
Shan Shan Wang
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Univariate Time Series ◽  
Higher Dimensional ◽  
Dimensional State Space ◽  
Phase Space Theory

In order to fully reveal information about landslide displacement, it was necessary to extend a time series to a higher-dimensional state space for the characteristic of univariate time series. However, in order to control the expansion of noise, an appropriate embedded dimension of phase space reconstruction was not the bigger the better. In this paper, based on the displacement time series of landslide, the phase space theory was used to build displacement time series matrix and the entropy theory was used to get the entropy. The embedded dimension of phase space reconstruction could be adjusted according to the change of entropy and feedback of displacement prediction error and a support vector regression model was created via the support vector machine’s learning. The application on Baijiabao landslide indicates that the proposed method achieves a high accuracy and stability of prediction.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

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