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Performance Ratio Estimation and Prediction of Solar Power Plants Using Machine Learning to Improve Energy Reliability
Bandong S.a, Leksono E.a, Purwarianti A.a, Joelianto E.a
a Engineering Physics Master Program, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.One of the reliability indicators of a solar cell system is the ratio of solar cell performance. The performance of the solar cell itself is obliged to be predicted so that energy providers can carry out certain plans related to the maintenance or replacement of solar cell systems if the efficiency of solar cells has been already obsolete. The prediction of solar cell performance can also be an indicator of potential failure in solar cell systems. Machine learning is used to estimate and to predict the performance ratio of solar cells. Principal Component Analysis – Support Vector Machine (PCA-SVM) is applied to estimate the performance ratio by using 35,227 rows of three years weather data from 2015-2018. Grid search is then applied to find optimal SVM parameters in estimating performance ratio. The collected data are also used to predict future performance ratio. Prior data decomposing of performance ratio is done to obtain data trend and eliminate noise which causes low prediction accuracy. SVM and Multiple Linear Regression are used to predict performances ratio using one-step time series method. MSE, RMSE, R2 and MAPE will be used to evaluate the best way for predicting performances ratio of solar cells plants. The PCA-SVM leads to the accuracy of RMSE = 0.11 and R2 = 0.44. For prediction, SVM and Multiple Linear Regression are compared to see machine learning that produces the best accuracy. Using the one-step method, SVM results in better prediction results compared to Multiple Linear Regression. Two weeks timestep prediction produces the smallest RMSE and MAPE and the highest R2 both by using SVM or Multiple Linear Regression.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Future performance,Multiple linear regressions,Performance ratio,Potential failures,Prediction accuracy,Reliability indicators,Solar cell performance,Solar Cell System[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Estimation,Machine Learning,Performance Ratio,Prediction,Solar Power Plant reliability[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2019.8916687[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]