Provincial Energy Efficiency Prediction in China Based on Classification Method

Abstract

Improving the development and utilization of renewable energy is one of the critical goals in the green industrial revolution of China. The energy efficiency measurement and the detection of influence factors are the very important issues to accomplish this task. In this paper, feature recognition is applied to determine the factors that affect the energy efficiency. Based on the feature recognition algorithm, data mining methods are used to derive knowledge from a dataset of 24 provinces or cities in China. In the process of data mining, the following three problems are addressed: 1) the optimal feature subset is selected from the original feature set that affects the energy efficiency; 2) the energy efficiency based on the optimal feature subset is evaluated; and 3) the energy efficiency in China is predicted by seven good-fitted classification models, whose accuracy rates are higher than 90%. Combining the results of feature selection and energy efficiency prediction, the strategy and policy to improve the energy efficiency in China is suggested. Green energy policies and prediction institutions of energy efficiency are really necessary in each province to improve their energy consumption structures, especially in coordinating the development between the traditional energy and renewable energy.

Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

“…Despite the rising awareness of global environmental issues, high energy consumption is arguably a colossal constituent of those issues. Namely, in the domestic sector, a vast expanse of research was done in the field of energy efficiency [2]- [5]. Works in the field deduced a number of influencers to the energy consumer's behavior towards (or against) energy-saving behavior [6]- [8].…”