论文年份：2019
论文原文：https://eprints.qut.edu.au/128957/
DOI：10.20944/preprints201903.0131.v1
文章目录
- Energy consumption prediction using machine learning; a review
- Abstract
- 1. Introduction
- 2. Research Methodology
- 3. Machine learning models
- ANFIS（Adaptive neuro fuzzy inference system）
- ANNs
- SVM and SVR
- WNNs（Wavelet neural networks）
- DTs
- ELMs（Extreme learning machine）
- MLPs
- ENSEMBLEs
- HYBRIDs
- DEEP LEARNINGs
- 4. Discussion
Energy consumption prediction using machine learning; a review
Abstract
| orig | trans |
|---|---|
| Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models. | 机器学习（ML）方法最近在用于能源消耗的预测模型的发展中做出了很大的贡献。这样的模型极大地提高了常规时间序列预测工具的准确性，鲁棒性和精度以及泛化能力。本文回顾了能源消耗的一般应用中使用的机器学习模型的最新状态。通过新颖的搜索和分类，根据ML建模技术，能量类型，消亡类型和应用领域对本领域中最相关的文献进行了分类。对文献的全面回顾指出了主要的机器学习方法，它们的应用以及对它们在能耗预测中的有效性评估的讨论。本文还对ML模型的趋势和有效性进行了总结。结果，这项研究报告了使用新型混合和集成预测模型的预测技术的准确性和不断提高的性能。 |
| Keywords: energy consumption; prediction; machine learning models; deep learning models; artificial intelligence (AI); computational intelligence (CI); forecasting; soft computing (SC); | 关键词：能耗预测；机器学习模型；深度学习模型；人工智能（AI）；计算智能（CI）；预测；软计算（SC）； |
1. Introduction
| Energy consumption is among one of the essential topics of energy systems. Energy consumption came under the consideration after the energy crisis in 1970s[1]. Also, It is shown that energy consumption throughout the world is rapidly increasing [2]. Therefore, each country tries to use as less energy as possible in their country in different areas from building to farms, from industrial process to vehicles [3]. As energy comes from three different resources like fossil fuels, renewable and nuclear resources [4], it need so much effort to keep tracking of energy consumption of these types in different area. However by doing so, we can predict the amount of energy, which is consumed in different areas and try to make plans, specialized for a specific usage and area. | 能源消耗是能源系统的基本主题之一。在1970年代的能源危机之后，能源消耗才被考虑在内[1]。同样，它表明，全世界的能源消耗都在迅速增加[2]。因此，每个国家在从建筑到农场，从工业过程到车辆的不同地区，都试图在本国使用尽可能少的能源[3]。由于能源来自化石燃料，可再生能源和核能等三种不同的资源[4]，因此需要付出巨大的努力来跟踪不同地区这些类型的能源消耗。但是，通过这样做，我们可以预测在不同区域中消耗的能量，并尝试制定针对特定用途和区域的计划。 |
| For all energy types mentioned above, estimating the usage is useful for decision and policy makers. By knowing how much energy will be used for their process or work, they can be able to think of some changes in them to reduce the amount energy usage. Predicting future energy usage both in Short-term and Long-term manner will help us even to know, that in which type energy is used mostly and try to change the trend, as it is happed in the recent years for fossil fuels and now we have renewable energy. The amount of energy used in different areas is influenced by diffent factors such as water, wind, temperature. Having multiple factors, predicting the energy consumption is a complex problem[5]. | 对于上述所有能源类型，估算使用量对于决策者和决策者都是有用的。通过知道将在他们的过程或工作中使用多少能量，他们可以想到其中的一些变化以减少能量使用量。预测短期和长期的未来能源使用量将帮助我们甚至知道，哪种类型的能源最常使用，并试图改变趋势，因为近年来化石燃料受到限制，现在我们有可再生能源。不同地区使用的能源量受水，风，温度等不同因素的影响。有多个因素，预测能耗是一个复杂的问题[5]。 |
| Nowadays ML models are being used in different areas because they are useful and the way ML works is like a function which best maps the input data to output. Machine learning models can produce prediction for enery consumption with high accuracy. So they can be used by governments to implement enery-saving policies. For instance, ML models can predict the amount enery used in building [6]. They can also be used to predict the future use of different types of energy like electricity or natural gas[6]. | 如今，ML模型已在不同领域中使用，因为它们非常有用，并且ML的工作方式就像一个函数，可以最好地将输入数据映射到输出。机器学习模型可以准确预测黑胶消耗量。因此，政府可以使用它们来实施节省能源的政策。例如，机器学习模型可以预测建筑物中使用的烯醇量[6]。它们还可以用于预测未来将使用不同类型的能源，例如电或天然气[6]。 |
| This research work has been conducted in the prediction of different enery type usage. Predictions can be done on the usage of energy in a specific procedure in industry [7] or the total amout of energu used in a coutry [8]. This study tries to review the recent studies related to modeling and estimating of energy consumption in different area. | 这项研究工作是在预测不同类型的使用情况下进行的。可以对工业中特定过程中的能源使用情况进行预测[7]，或者对国家中使用的能源总量进行预测[8]。本研究试图回顾与不同地区能源消耗的建模和估算有关的最新研究。 |
| The organization of this paper is in a way to review different ML models for energy prediction like: ANFIS, ANN, DT, ELM, MLP, SVM/SVR, WNN, ENSEMBLE, HYBRID, and DEEP LEARNING. In each section related to each model we try to review the latest studies which uses ML models for forecasting energy usage in different applications. | 本文的组织方式是回顾用于能量预测的不同ML模型，例如：ANFIS，ANN，DT，ELM，MLP，SVM / SVR，WNN，ENSEMBLE，HYBRID和DEEP LEARNING。在与每个模型有关的每个部分中，我们尝试回顾使用ML模型预测不同应用中的能源使用情况的最新研究。 |
2. Research Methodology
The database is created using the following search keywords to identify the manuscripts on energy consumption prediction using machine learning models. ISI and Scopus databases had been explored using the essential search keywords, i.e., ( TITLE-ABS-KEY ( “energy consumption” ) AND TITLE-ABS-KEY ( “machine learning” OR “Deep learning” OR “ANN” OR “MLP” OR “ELM” OR “neural network” OR “ANFIS” OR “decision tree” OR wnn ) ).
使用以下搜索关键字创建数据库，以使用机器学习模型识别有关能耗预测的手稿。使用必要的搜索关键字对ISI和Scopus数据库进行了探索，即（TITLE-ABS-KEY（“能源消耗”）和TITLE-ABS-KEY（“机器学习”或“深度学习”或“ ANN”或“ MLP “或” ELM“或”神经网络“或” ANFIS“或”决策树“或wnn））。
Study the database shoes a dramatic increase in the number of papers from 2006 to 2018. The database include 4300 papers. The most relevant and original works have been revised in this stateof-the-art.
从2006年到2018年，研究数据库的论文数量急剧增加。该数据库包含4300篇论文。在此最新技术中，最相关和最原始的作品已经过修订。
3. Machine learning models
Here comes the taxonomy chart and one paragraph explanation. The ML models include: ANFIS, ANN, DT, ELM, MLP, SVM/SVR, WNN, ENSEMBLE, HYBRID, and DEEP LEARNING.
ANFIS（Adaptive neuro fuzzy inference system）
ANNs
SVM and SVR
WNNs（Wavelet neural networks）
DTs
ELMs（Extreme learning machine）
MLPs
ENSEMBLEs
HYBRIDs
DEEP LEARNINGs
4. Discussion
The use of ML models have been increased during the last decade. Along with the conventional ML methods, e.g., ANNs and MLP, DTs, the application of hybrids and Ensemble methods has been dramatically increased. Through hybrid methods the researcher aim at higher accuracy and efficiency. The future direction of the research is to develop hybrid models with higher accuracy and higher speeds.
在过去十年中，机器学习模型的使用有所增加。与传统的ML方法（例如ANN和MLP，DT）一起，混合和集成方法的应用已大大增加。通过混合方法，研究人员致力于更高的准确性和效率。研究的未来方向是开发具有更高准确性和更高速度的混合模型。