Improving the Performance of Machine Learning Model Selection for Electricity Cost Forecasting in Homebased Small Businesses via Exploratory Data Analysis
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An Exploratory Data Analysis (EDA) remains a major tool proving a better understanding of the time series dataset with assumptions that could culminate in obvious errors being eliminated. Yet this step during modeling is often done in a less exhaustive manner. This has resulted in assumptions without effective uncovering of the relationships and structure of the underlying dataset. Besides, possible trends and patterns that might not be readily apparent are often not exposed due to the lack of a comprehensive EDA. The primary step to forecasting entails the selection of the appropriate forecasting approach which remains a major challenge for analysts performing the forecasts. This challenge is the reason why many researchers continue to propose new approaches with the objective of forecasting accuracy enhancements. This study examines the impact of applying a robust EDA in improving Machine Learning model selection for electricity cost forecasting in home-based small businesses.
-
Abbas HA. Future SCADA challenges and the promising solution: the agent-based SCADA. International Journal of Critical Infrastructures. 2014;10(3-4):307-33.
Google Scholar -
Apuke OD. Quantitative research methods: A synopsis approach. Kuwait Chapter. Arabian Journal of Business and Management Review. 2017 Sep;33(5471):1-8.
Google Scholar -
Baker C. Quantitative research designs: Experimental, quasi-experimental, and descriptive. Evidence-based practice: An integrative approach to research, administration, and practice. 2017:155-83.
Google Scholar -
Bloomfield J, Fisher MJ. Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association. 2019;22(2):27-30.
Google Scholar -
Gudfinnsson K, Strand M. Challenges with BI adoption in SMEs. In 2017 8th International Conference on Information, Intelligence, Systems & Applications, (IISA), 2017 Aug 27 (pp. 1-6). IEEE.
Google Scholar -
Khan M, Wu X, Xu X, Dou W. Big data challenges and opportunities in the hype of Industry 4.0. In 2017 IEEE International Conference on Communications (ICC) 2017 May 21 (pp. 1-6). IEEE.
Google Scholar -
Kiangala SK, Wang Z. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment. Machine Learning with Applications. 2021 Jun 15;4:100024.
Google Scholar -
Krishnan G. Marketers, Big Data and Intuition–Implications for Strategy and Decision-Making. The University of Liverpool (United Kingdom); 2018.
Google Scholar -
Lee HS, Choi Y. Environmental performance evaluation of the Korean manufacturing industry based on sequential DEA. Sustainability. 2019 Feb 7;11(3):874.
Google Scholar -
Llave MR. Business intelligence and analytics in small and medium-sized enterprises: A systematic literature review. Procedia Computer Science. 2017 Jan 1;121:194-205.
Google Scholar -
Mahajan A, Madhani P, Chitikeshi S, Selvaganesan P, Russell A, Mahajan P. Advanced Data Analytics for Improved Decision-Making at a Veterans Affairs Medical Center. Journal of Healthcare Management. 2019 Jan 1;64(1):54-62.
Google Scholar -
Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PloS one. 2018 Mar 27;13(3):e0194889.
Google Scholar -
McKinley DJ. Leveraging “Small Data” through Predictive Analytics to Support Clinical Decision-Making. [Doctoral dissertation] Mercer University.
Google Scholar -
Ren S, Choi TM, Liu N. Fashion sales forecasting with a panel data-based particle-filter model. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2014 Aug 26;45(3):411-21.
Google Scholar -
Ren S, Chan HL, Siqin T. Demand forecasting in retail operations for fashionable products: methods, practices, and real case study. Annals of Operations Research. 2020 Aug;291(1):761-77.
Google Scholar -
Ye Y, Wang M, Yao S, Jiang JN, Liu Q. Big data processing framework for manufacturing. Procedia CIRP. 2019 Jan 1;83:661-4.
Google Scholar -
Zhao Y, Li Y, Zhang X, Geng G, Zhang W, Sun Y. A survey of networking applications applying the software defined networking concept based on machine learning. IEEE Access. 2019 Jul 15;7:95397-417.
Google Scholar
[1]
Agbor, E.A. and Qu, Y. 2023. Improving the Performance of Machine Learning Model Selection for Electricity Cost Forecasting in Homebased Small Businesses via Exploratory Data Analysis. European Journal of Electrical Engineering and Computer Science. 7, 2 (Mar. 2023), 7–14. DOI:https://doi.org/10.24018/ejece.2023.7.2.481.
