A review on time series forecasting techniques for building energy consumption

Deb, Chirag; Zhang, Fan; Yang, Junjing; Lee, Siew Eang; Shah, Kwok Wei

doi:10.1016/j.rser.2017.02.085

Cited by 738 publications

(372 citation statements)

References 160 publications

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“…They conducted a series of tests ranging from unit root test to cointegration test to guarantee that a cointegration relationship exists between energy demand and its factors (i.e., the nexus will not change in the medium and long term). The ARIMA model is presented as an appropriate method for long-term projections [4][5][6][7][8]25]. This model depends on three parameters, including order of moving average, order of differencing, and order of autoregressive scheme.…”

Section: Econometric Methodmentioning

confidence: 99%

“…In general, these early studies can be classified into two major categories: econometric [2][3][4][5][6][7][8][9] and machine learning (ML) methods [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The artificial intelligence (AI) energy forecasting model, which is a class of ML method, has gained popularity in recent years because of its superiority in time series processing and its capability to deal with noise data.…”

Section: Introductionmentioning

confidence: 99%

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Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Huang

Tang

Chen

2018

Mathematical Problems in Engineering

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Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985-2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

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Section: Econometric Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Huang

Tang

Chen

2018

Mathematical Problems in Engineering

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“…However, in reality, occupants' behavior is more complex to be captured by only two parameters. The works in [26,27] are surveys of existing studies, does not discuss on occupants' behavior or appliance level analysis, all the models discussed are at the premise, building, or even national level. Similarly, [28] use Support Vector Regression (SVR) and [29] use weighted Support Vector Regression (SVR) with nu-SVR and epsilon-SVR while using differential evolution (DE) algorithm for selecting parameters to forecast electricity consumption at daily and 30 min at a building level.…”

Section: Introductionmentioning

confidence: 99%

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018

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Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers' energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

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“…The forecasting of time series is extensively covered in numerous scientific works [15][16][17][18][20][21][22][23][24]. In the case of non-linear systems and systems whose state varies dynamically, being influenced by their actual state, methods like the nonlinear autoregressive and the nonlinear autoregressive with exogenous inputs proved to be very efficient in forecasting future values of time series [19,25].…”

Section: Introductionmentioning

confidence: 99%

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

et al. 2017

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This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

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A review on time series forecasting techniques for building energy consumption

Cited by 738 publications

References 160 publications

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

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