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1. Forecasting household electricity demand in India: A long short-term memory approach.

Author

Pallaiyah, Solainayagi, Kumar, Ankit, Raj, Adity, Kumar, Rohan, Nihal, Muhammed, Rajendran, Mohanapriya, and Govindaraj, Jijina

Subjects
*SUSTAINABILITY, *CLEAN energy, *CONSUMPTION (Economics), *ENERGY consumption, *HOUSEHOLDS, *FORECASTING, *ELECTRIC power consumption
Abstract

With the increasing demand for energy worldwide, it has become imperative to explore and adopt sustainable energy practices. In this regard, predicting household electricity consumption accurately is essential, as it can help optimize energy usage and reduce waste. This paper focuses on using Long Short-Term Memory (LSTM) shows the neural networks to prevent household electricity consumption in India. The LSTM model was implemented using the Keras library in Python. The model was trained using the training set and tested using the testing set. The study demonstrated that LSTM neural networks are a promising approach to predicting household electricity consumption in India. Further research can be conducted to recover the accuracy of the perfect by including more variables such as household size, income, and appliance usage. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Examining energy justice: Empirical analysis of clean cooking transition across social groups in India, 2004–2018.

Author

Rahut, Dil Bahadur, Aryal, Jeetendra Prakash, Manchanda, Navneet, and Sonobe, Tetsushi

Subjects
*SOCIAL groups, *POOR families, *ENERGY consumption, *LOGISTIC regression analysis, *INSTRUMENTAL variables (Statistics), *CLEAN energy, DEVELOPING countries
Abstract

Literature has highlighted the existence of a gap in clean fuel usage between the social groups in most emerging nations across the world, which is detrimental to global efforts to achieve Sustainable Development Goal (SDG) 7 (access to clean energy) and SDG 10 (reducing inequalities) and just energy transition with recognition justice. This paper, assesses the clean fuel gap across social groups in India using three rounds of representative data (2004, 2014, and 2018) by applying the logit model, exogenous switching treatment effect regression (ESTER), and the Blinder–Oaxaca Decomposition Model. Such an analysis is instrumental in identifying the clean energy usage gap and its causes, thereby providing insight to policymakers to design policies to achieve just energy transition and carbon neutrality by 2060. The results show a substantive clean fuel gap between social groups in India, which is a cause of concern for just energy transition. Clean energy utilization gaps among social groups are alarming across geographical locations and consumption quartiles, highlighting the importance of recognition justice for just energy transition. The results show that in rural areas, only 19.6 % of the General Caste households and 14.5 % of the disadvantaged social groups (Scheduled Tribes, Scheduled Castes, and Other Backward Class) in quartile one use clean energy, while in urban areas, the shares are 60.4 % and 46.3 %, respectively. To achieve a just distribution of the benefits of energy transition, energy policies should focus on disadvantaged social groups and simultaneously target General Caste households from a lower economic status and those residing in rural areas, indicating the need for targeting the neglected regions. [Display omitted] • Clean energy use has steadily increased among all social groups in India, indicating some improvement in energy justice. • Clean energy access gap among social groups has declined, but this gap still persists. • Scheduled Castes and Scheduled Tribes are the most disadvantaged social groups in terms of clean energy. • All social groups from low-income families have limited access to clean energy. • All social groups from households with low education have limited access to clean energy. [ABSTRACT FROM AUTHOR]

Published
2024
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