Abstract
With the global increase in demand for energy, energy conservation of research and development buildings has become of primary importance for building owners. Knowledge based on the patterns in energy consumption of previous years could be used to predict the near-future energy usage of buildings, to optimize and facilitate more effective energy consumption. Hence, this research aimed to develop a generic model for predicting energy consumption. Air-conditioning was used to exemplify the generic model for electricity consumption, as it is the process that often consumes the most energy in a public building. The purpose of this paper is to present this model and the related findings. After causative factors were determined, the methods of linear regression and various machine learning techniques—including the earlier machine learning techniques of support vector machine, random forest, and multilayer perceptron, and the later machine learning techniques of deep neural network, recurrent neural network, long short-term memory, and gated recurrent unit—were applied for prediction. Among them, the prediction of random forest resulted in an R2 of 88% ahead of the first month and 81% ahead of the third month. These experimental results demonstrate that the prediction model is reliable and significantly accurate. Building owners could further enrich the model for energy conservation and management.
Highlights
The ongoing global economic development has increasingly consumed energy resource
The purpose of this paper is to present the research results based on the use of multiple machine learning (ML) techniques and to propose a generic modelling process for predicting electricity consumption
After screening factors in this research, the prediction model of air-conditioning electricity consumption was constructed by six factors in eight models
Summary
The ongoing global economic development has increasingly consumed energy resource. Based on statistical Review of World Energy, global primary energy consumption grew rapidly in 2018, and at a rate of 2.9% last year, almost double its 10-year average of 1.5% per year, and the fastest since 2010 [1].according to the Stated Policies Scenario, electricity use has been growing at more than double the pace of overall energy demand, confirming its place at the heart of modern economies [2].For the sake of sustainable development and mitigating the depletion of energy resources, energy conservation has become critical task during economic development.The Ministry of Economic Affairs at Taiwan reported that the national electricity consumption in 2018 was about 264.3 billion kWh. The ongoing global economic development has increasingly consumed energy resource. According to the Stated Policies Scenario, electricity use has been growing at more than double the pace of overall energy demand, confirming its place at the heart of modern economies [2]. The Ministry of Economic Affairs at Taiwan reported that the national electricity consumption in 2018 was about 264.3 billion kWh. The energy consumed by various buildings each year accounted for more than one-third of the national energy ratio, and the proportion grows year by year [3]. The owners of public buildings with high electricity consumption [4] in Taiwan are often the main target for promoting public policies for energy conservation. The optimal operation of the buildings is crucial for reducing electricity consumption. Optimizing buildings’ electricity consumption will be Energies 2020, 13, 1847; doi:10.3390/en13071847 www.mdpi.com/journal/energies
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