Industrial Management

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    Forecasting of Medium-Term Energy Output of On-Grid Rooftop Photovoltaic Arrays -Case Study for a Sri Lankan Solar Panel InstallerSenanayake, Janaka
    (Institute of Electrical and Electronics Engineers (IEEE), 2023) Wickramasinghe, Bhagya; Asanka, PPG Dinesh
    The world is shifting towards the higher utilization of renewable energy sources in the road to greener energy which conserves an environmentally friendly atmosphere. The generation of sustainable energy via adopting solar photovoltaic is common worldwide. The objectives of the research study are to identify the salient factors contributing to the energy generation of photovoltaic systems, to utilize a gamut of machine learning algorithms to build the predictive model and to identify the best machine learning algorithm to predict the energy generation based on accuracy and precision metrices. These objectives aid to achieve the aim of this study, which is to build a predictive model to determine the medium-term energy generated from on-grid rooftop solar systems. The study has unveiled a new piece of knowledge on how the photovoltaic system dynamics and location specific data has contributed to the prediction of the power output of the system. Further the findings are of paramount importance to the industry experts as well as the current and prospective solar panel users. The data of all solar panel sites of the installer was utilized and it was extracted from the source information systems. The necessary transformations and validations were applied and a detailed analysis was performed. The feature engineering, feature scaling, outlier-handling, multi-collinearity and feature selection was performed on data. The intended forecasting model based on fourteen supervised machine learning algorithms was built. The KNN Regression algorithm in the factor analysis of all features after principal component analysis has outperformed all other built models. Moreover, a strong positive co-relation was observed in the principal component analysis towards the solar panel energy output prediction. As part of future work, it’s imperative to build models utilizing a wider sample of on-grid roof top solar plants.
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    Identifying Unusual Human Movements Using Multi-Agent and Time-Series Outlier Detection Techniques
    (Institute of Electrical and Electronics Engineers (IEEE), 2023) Asanka, PPG Dinesh; Rajapakshe, Chathura; Takahashi, Masakazu
    This research paper has introduced knowledgedriven multi-agent technology for automated machine learning in time series analysis in the context of human mobility. The main objective of this research is to identify unusual human mobility using Time Series outlier detection techniques with a more efficient multi-agent system. Detection of unusual human movement can be helpful for many domains, such as security, marketing, and health. A mobile dataset in Hiroshima, Japan between 2019-December to 2020-November was used for this research. The mobile dataset was converted to time series for multiple locations in Hiroshima, Japan. Since many different parameters are selected for time series, the message space multiagent technique is used. Sub agents are introduced for duplicate removal, missing data replacement, and outlier detection. Multiple processing agents and a control agent were introduced to predict the missing values to improve the efficiency of the model. Finally, using the Seasonal-Trend decomposition techniques, unusual movements are identified, and unusual human movements are plotted with the holidays. Multiple outlier points were detected for all the locations, and there were more than a hundred outlier points were detected for the selected locations.