Evaluation of different statistical techniques for developing cost correlations of micro hydro power plants. (February 2021)

• Record Type: Journal Article
• Authors: Malhan, Priyanka; Mittal, Monika
• Is Part Of: Sustainable energy technologies and assessments. Volume 43(2021)
• Publication Date: 2021-02
• Subjects: SHP Small Hydro Project -- MHP Micro Hydro Project -- MHPP Micro Hydro Power Plant -- Ccw Cost of civil works -- Cem Cost of electro-mechanical equipment -- E&M Electro-mechanical equipment -- C Capital cost of project -- MAPE Mean Absolute Percentage Error; Micro hydro projects -- Project cost -- Electromechanical equipment cost -- Civil works cost; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2020.100904
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Correlations are developed for all major cost components of micro hydro power plants. Linear regression approach used for small hydro plants is tested for MHPs. Multivariate polynomial regression model yields better results for MHPs. Abstract: The ministry of India has proposed the SHP (Small Hydro Power) scheme to achieve a cumulative capacity of 6000 MW from small and micro hydro projects (MHPs) till 2022. Initial stage investigation of these projects require accurate investment cost estimates to carry out the feasibility studies. Although many previous research studies developed various cost estimation models for SHPs, they can't be directly employed for cost estimation of micro hydropower as well without their comprehensive evaluation. Therefore, this paper focuses on estimation of cost correlations for MHPs using different statistical methods to test the viability of these methods and to present a distinctive approach for more accurate cost estimates. This study meticulously analyzes a total of three methods; the former two methods utilize the existing approaches to evaluate each cost component individually in order to derive the respective cost expressions. And, the third method explores the effects of employing multivariate polynomial regression model for the development of cost correlations, which is validated further on the basis of MAPE (mean absolute percentage error). The proposed method yields an MAPE of less than 5% for total project cost of MHPs while it is found more than 20% using aforesaid methods.