scholarly journals Forecasting monthly energy production of small hydropower plants in ungauged basins using grey model and improved seasonal index

2017 ◽  
Vol 19 (6) ◽  
pp. 993-1008 ◽  
Author(s):  
Chun-Tian Cheng ◽  
Shu-Min Miao ◽  
Bin Luo ◽  
Yong-Jun Sun
Keyword(s):  
Energy Production ◽  
Moving Average ◽  
Grey Model ◽  
Small Hydropower ◽  
Ungauged Basin ◽  
Model Combining ◽  
Hydropower Plants ◽  
Study Results ◽  
Seasonal Index

Abstract A first-order one-variable grey model (GM(1,1)) is combined with improved seasonal index (ISI) to forecast monthly energy production for small hydropower plants (SHPs) in an ungauged basin, in which the ISI is used to weaken the seasonality of input data for the GM(1,1) model. The ISI is calculated by a hybrid model combining K-means clustering technique and ratio-to-moving-average method, which can adapt to different inflow scenarios. Based on the similar hydrological and meteorological conditions of large hydropower plants (LHPs) and SHPs in the same basin, a reference LHP is identified and its local inflow data, instead of the limited available data of SHPs, is used to calculate the ISI. Case study results for the Yangbi and Yingjiang counties in Yunnan Province, China are evaluated against observed data. Compared with the original GM(1,1) model, the GM(1,1) model combined with traditional seasonal index (TSI-GM(1,1)), and the linear regression model, the proposed ISI-GM(1,1) model gives the best performance, suggesting that it is a feasible way to forecast monthly energy production for SHPs in data-sparse areas.

scholarly journals PREDIKSI KREDIT DAN INDEKS MUSIMAN (SEASONAL INDEX) PADA BANK PERKREDITAN RAKYAT DI KABUPATEN JEMBER

BISMA ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 210
Author(s):  
Hari Sukarno ◽  
Ratna Pratiwi Nugroho ◽  
Susanti Prasetiyaningtiyas
Keyword(s):  
Moving Average ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Development Pattern ◽  
Research Approach ◽  
Financial Reports ◽  
Double Exponential ◽  
Study Results ◽  
Double Exponential Smoothing ◽  
Seasonal Index

This research aims to analyze the credit's predictive value, the development pattern of credit distribution, and the credit fluctuations of 13 Rural Banks in Jember, influenced by seasonal index variables, credit interest, NPL, LDR, ROA, CAR, and operational efficiency ratio. This study used an explanatory research approach. The sample consisted of all Rural Banks' quarterly financial reports in 2014-2019 taken by a purposive sampling method. Data were analyzed using three methods, i.e., double exponential smoothing, moving average ratio, and multiple linear regression analysis methods. Results showed that, according to each data analysis method, ten Rural Banks experienced increased credit distribution. However, the other three Rural Banks experienced a decrease in credit distribution. The study results also indicated an increasing trend in the development pattern of credit distribution. Meanwhile, the NPL and LDR variables partially influenced credit fluctuations. Keywords: credit prediction, rural bank, seasonal index

scholarly journals An Improved Metabolism Grey Model for Predicting Small Samples with a Singular Datum and Its Application to Sulfur Dioxide Emissions in China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Zhou ◽  
Demei Zhang
Keyword(s):  
Sulfur Dioxide ◽  
Grey Model ◽  
Small Samples ◽  
Grey Prediction ◽  
Economic Data ◽  
Time Data ◽  
Sulfur Dioxide Emissions ◽  
Study Results ◽  
Standard Level

This study proposes an improved metabolism grey model [IMGM(1,1)] to predict small samples with a singular datum, which is a common phenomenon in daily economic data. This new model combines the fitting advantage of the conventional GM(1,1)in small samples and the additional advantages of the MGM(1,1)in new real-time data, while overcoming the limitations of both the conventional GM(1,1)and MGM(1,1)when the predicted results are vulnerable at any singular datum. Thus, this model can be classified as an improved grey prediction model. Its improvements are illustrated through a case study of sulfur dioxide emissions in China from 2007 to 2013 with a singular datum in 2011. Some features of this model are presented based on the error analysis in the case study. Results suggest that if action is not taken immediately, sulfur dioxide emissions in 2016 will surpass the standard level required by the Twelfth Five-Year Plan proposed by the China State Council.

scholarly journals Environmental Flows Assessment in Nepal: The Case of Kaligandaki River

2020 ◽  
Vol 12 (21) ◽  
pp. 8766 ◽  
Author(s):  
Naresh Suwal ◽  
Alban Kuriqi ◽  
Xianfeng Huang ◽  
João Delgado ◽  
Dariusz Młyński ◽  
...  
Keyword(s):  
Environmental Flows ◽  
Environmental Flow ◽  
Trophic Levels ◽  
Flow Duration Curve ◽  
Small Hydropower ◽  
Distribution Method ◽  
Flow Duration ◽  
Hydropower Plants ◽  
Study Results ◽  
Annual Distribution

Environmental flow assessments (e-flows) are relatively new practices, especially in developing countries such as Nepal. This study presents a comprehensive analysis of the influence of hydrologically based e-flow methods in the natural flow regime. The study used different hydrological-based methods, namely, the Global Environmental Flow Calculator, the Tennant method, the flow duration curve method, the dynamic method, the mean annual flow method, and the annual distribution method to allocate e-flows in the Kaligandaki River. The most common practice for setting e-flows consists of allocating a specific percentage of mean annual flow or portion of flow derived from specific percentiles of the flow duration curve. However, e-flow releases should mimic the river’s intra-annual variability to meet the specific ecological function at different river trophic levels and in different periods over a year covering biotas life stages. The suitability of the methods was analyzed using the Indicators of Hydrological Alterations and e-flows components. The annual distribution method and the 30%Q-D (30% of daily discharge) methods showed a low alteration at the five global indexes for each group of Indicators of Hydrological Alterations and e-flows components, which allowed us to conclude that these methods are superior to the other methods. Hence, the study results concluded that 30%Q-D and annual distribution methods are more suitable for the e-flows implementation to meet the riverine ecosystem’s annual dynamic demand to maintain the river’s health. This case study can be used as a guideline to allocate e-flows in the Kaligandaki River, particularly for small hydropower plants.

Setting the problem of energy production forecasting for small hydropower plants in the Target Model era

2021 ◽  
Author(s):  
Korina Konstantina Drakaki ◽  
Georgia-Konstantina Sakki ◽  
Ioannis Tsoukalas ◽  
Panagiotis Kossieris ◽  
Andreas Efstratiadis
Keyword(s):  
Electricity Market ◽  
Energy Production ◽  
Data Availability ◽  
Sources Of Information ◽  
Small Hydropower ◽  
Target Model ◽  
Production Forecasting ◽  
Hydropower Plants ◽  
Hydropower Production ◽  
Alternative Sources

<p>The highly-competitive electricity market over EU and the challenges induced by the so-called “Target Model”, introduce significant uncertainties to day-ahead trades involving renewable energy, since most of these sources are driven by non-controllable weather processes (wind, solar, hydro). Here, we explore the case of small hydropower plants that have negligible storage capacity, and thus their production is just a nonlinear transformation of inflows. We discuss different forecasting approaches, which take advantage of  alternative sources of information, depending on data availability. Among others, we investigate whether is it preferable to employ day-ahead predictions based on past energy production data per se, or use these data in order to retrieve past inflows, which allows for introducing hydrological knowledge within predictions. Overall objective is to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of hydropower production, thus quantifying the overall uncertainty of each forecasting method. Power forecasts are evaluated in terms of economic efficiency, accounting for the impacts of over- and under-estimations in the real-world electricity market.</p>

scholarly journals Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data

2022 ◽  
Vol 56 ◽  
pp. 155-162
Author(s):  
Korina-Konstantina Drakaki ◽  
Georgia-Konstantina Sakki ◽  
Ioannis Tsoukalas ◽  
Panagiotis Kossieris ◽  
Andreas Efstratiadis
Keyword(s):  
Electricity Market ◽  
Energy Production ◽  
Hydrological Regime ◽  
Power Production ◽  
Training Procedure ◽  
Small Hydropower ◽  
Hydropower Plants ◽  
Model Training ◽  
Different Levels ◽  
Run Of River

Abstract. Motivated by the challenges induced by the so-called Target Model and the associated changes to the current structure of the energy market, we revisit the problem of day-ahead prediction of power production from Small Hydropower Plants (SHPPs) without storage capacity. Using as an example a typical run-of-river SHPP in Western Greece, we test alternative forecasting schemes (from regression-based to machine learning) that take advantage of different levels of information. In this respect, we investigate whether it is preferable to use as predictor the known energy production of previous days, or to predict the day-ahead inflows and next estimate the resulting energy production via simulation. Our analyses indicate that the second approach becomes clearly more advantageous when the expert's knowledge about the hydrological regime and the technical characteristics of the SHPP is incorporated within the model training procedure. Beyond these, we also focus on the predictive uncertainty that characterize such forecasts, with overarching objective to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of power production. Finally, we discuss the use of the proposed forecasting procedure under uncertainty in the real-world electricity market.

scholarly journals The influence of hydrotechnical conditions on energy production in small-scale hydropower plants / Wpływ warunków hydrotechnicznych na wielkość produkcji energii w małych elektrowniach wodnych

2013 ◽  
Vol 18 (9) ◽  
pp. 29-35
Author(s):  
Marcin Bukowski
Keyword(s):  
Energy Production ◽  
Biomass Combustion ◽  
Small Scale ◽  
Small Hydropower ◽  
Total Energy Consumption ◽  
Factors Affecting ◽  
Technical Parameters ◽  
Energy Factors ◽  
Hydropower Plants ◽  
The Eu

Abstract Polish accession to the EU was followed by a need of adaptation of Polish legislation to the European requirements, also with regard to the energetic sector. The need of achieving 15% share of electric power from renewable sources in the total energy consumption till the year 2010 is a consequence of this decision. This target may be achieved in Polish conditions based on water and wind energy and from biomass combustion. The paper presents the influence of hydrologic conditions and technical parameters on the amount of produced energy. Factors affecting energy production in small hydropower plants were analysed. The formula was proposed to describe the effect of water flow in a river on energy production in small hydropower plants.

scholarly journals Optimal selection of pumps as turbines for maximizing electrical energy production

2021 ◽  
Vol 238 ◽  
pp. 01005
Author(s):  
Lucrezia Manservigi ◽  
Mauro Venturini ◽  
Enzo Losi
Keyword(s):  
Energy Production ◽  
Water Distribution ◽  
General Procedure ◽  
Water Distribution Network ◽  
Cost Effective ◽  
Electrical Energy ◽  
Small Hydropower ◽  
Hydropower Plants ◽  
Performance Curves ◽  
Electrical Energy Production

A Pump as Turbine (PAT) is a renewable energy technology that can be a cost-effective and reliable alternative to hydraulic turbines in micro and small hydropower plants. In order to further favour PAT exploitation, a general procedure that allows the identification of the most suitable turbomachine to install is required. To this purpose, this paper develops a novel methodology aimed at selecting the best PAT that, among several alternatives, maximizes energy production. The methodology comprises two steps, which only require the knowledge of the best efficiency point of the considered pump and the hydraulic parameters of the site. The novel methodology is validated in this paper by calculating the electrical energy production of a simulated water distribution network coupled with several PATs, whose performance curves, both in direct and reverse modes, are taken from the literature. For the sake of generality, the considered turbomachines account for different geometrical characteristics, rotational speeds and operating ranges.

Using seasonal forecast for energy production: SHYMAT climate service, a small hydropower management and assessment tool

2020 ◽  
Author(s):  
Eva Contreras Arribas ◽  
Javier Herrero Lantarón ◽  
Cristina Aguilar Porro ◽  
María José Polo Gómez
Keyword(s):  
Energy Production ◽  
Assessment Tool ◽  
Environmental Flow ◽  
Generation Process ◽  
Small Hydropower ◽  
Operation Conditions ◽  
Hydropower Plants ◽  
Climate Service ◽  
Decision Support Process ◽  
Operation Center

<p>In small hydropower plants management, the operation feasibility is subjected to the Run-of-River (RoR) flow which is also depending on a high variability in water availability. The management has to accomplish with some particular operation conditions of the plant but also some environmental flow requirements. Normally hydropower plants managers use historical information of inflows in order to predict the production of energy. Although some forecast models have been already proposed and applied in the small hydropower production field, there are still an existing gap to link the results of the forecast with the decision support process. </p><p>In the framework of the H2020 project CLARA (Climate forecast enabled knowledge services) a climate service was developed in a co-generation process, bridging the gap between data providers who provides climate-impact data on one side, and managers and policy makers on the other side. The result is SHYMAT (Small Hydropower Management and Assessment Tool), a technological solution for the integrated management of RoR plants which offers a scalable and automatically updated database accessible through an administration panel and a web end user interface. </p><p>The pilot area is a three RoR system in the Poqueira River (southern Spain) where inflow is highly variable due to the irregularity in precipitation and snow cover duration in the contributing basin. The service combines past hydro-meteorological and forecast climate data stored with operation data for the particular plant in order to give the user a) a global view of the hydrological state of the basin, from measurements and a physically based hydrological model; b) a comparison of current information with past data; c) the expected operability of the RoR plant; d) information about the accomplishment of environmental flow requirements and water flow spill; e) the expected energy production. </p><p>SHYMAT is easy and fully scalable to new systems thanks to the administration panel and the topology panel. The service is addressed to technicians in charge of the control operation center of this kind of plants and managers at the regional administrative headquarters of hydropower companies. Energy market operators, river basin authorities and consultants can be also potential users.</p><p> </p><p>This research is supported by CLARA Project, which has received funding from the European Union's Horizon 2020 research and innovation programme under the Gran Agreement No 730482.</p>

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