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

Abstract

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.

Inappropriate machine learning application in real power industry cases

“…When selecting relevant data sources, the lack of their preprocessing is more likely to lead to low accuracy of the developed model and low speed of operation of such a system than to systematic errors (provided that we are not pursuing real time operation of the system). For example, within the author's research, it was revealed that the absence of data preprocessing stage, on average, reduces the accuracy of the photovoltaic power plants generation forecast by 20-25% [7].…”

“…Photovoltaic power plants demonstrate the highest dynamics of growth among renewable-based power plants worldwide [7], [20]. The relevance of the problem being solved has no doubts.…”

Rasterized Data Image Processing (RDIP) Techniques for Photovoltaic (PV) Data Cleaning and Application in Power Prediction

“…With increasing global attention on renewable energy, photovoltaic (PV) power generation has garnered widespread attention and application as a pivotal solution to address the global energy demand, owing to its clean and sustainable nature. However, the operation and monitoring of PV power generation systems often generate a large amount of data, which may contain missing values, outliers, and noise, posing significant challenges for data analysis and application [1][2][3][4][5]. Therefore, PV data cleaning plays a crucial role in ensuring data quality, improving data availability and reliability [6,7].…”

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

“…In the last few years, we have also seen a transaction toward green alternatives for energy generations. In order to face this transactions, the photovoltaic power plants' forecasting problem was tackled in [4]. Authors of this article took into consideration various external factors, like meteorological situations in order to produce the predictions.…”