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1. Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network.

Author

Mayilsamy, Kathiresh, A, Maideen Abdhulkader Jeylani, Akbarali, Mahaboob Subahani, and Sathiyanarayanan, Haripranesh

Subjects
*LOAD forecasting (Electric power systems), *ALGORITHMS, *LINEAR statistical models, *MOVING average process, *STANDARD deviations, *FORECASTING
Abstract

Purpose: The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach: Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings: The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value: The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Dynamic Simulation of Eastern Regional Grid of India using Power System Simulator for Engineering PSS®E.

Author

Gogoi, Kamaljyoti and Chatterjee, Saibal

Subjects
*DYNAMIC simulation, *SYSTEMS engineering, *ELECTRIC lines, *ALGORITHMS, *FORECASTING
Abstract

Eastern Regional (ER) grid of India is one of the five regional grids consisting of voltage level 765 kV, 400 kV, 220 kV and numerous buses at lower voltage range. ER grid is the backbone of the Indian Grid as it is one of the main power generating hubs. Stability of the Indian Grid depends a lot on ER grid as it is the core of the generation and transmission system. For performing stability studies and to determine the weak area of the grid generally L-Index method is used. For detailed analysis like computation of voltage recovery time, maximum frequency deviation and critical clearing time of the buses of the system L-Index method is not suitable. Hence dynamic simulation is performed as it provides options to compute various parameters of ER Grid. The system has been studied using 197 buses of voltage levels 220 kV and above and transmission lines running over a length of 23,693 km. In this paper the power flow modelling, simulation and analysis of the Eastern Grid of India is performed. L-Index algorithm along with dynamic simulation provides the scope of computation of voltage recovery time, maximum frequency deviation and critical clearing time (by introducing an initial transient disturbance) along with the prediction of weak area of the system which gives a complete picture of transient stability of ER Grid. [ABSTRACT FROM AUTHOR]

Published
2020
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3. A novel grey Riccati–Bernoulli model and its application for the clean energy consumption prediction.

Author

Xiao, Qinzi, Gao, Mingyun, Xiao, Xinping, and Goh, Mark

Subjects
*ENERGY consumption, *CLEAN energy, *ENERGY consumption forecasting, *FORECASTING, *ALGORITHMS
Abstract

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

Published
2020
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