A review on applications of ANN and SVM for building electrical energy consumption forecasting

Ahmad, Ahmad Sukri; Hassan, Mohammad Yusri; Abdullah, Md. Pauzi; Rahman, Hasimah Abdul; Hussin, Faridah; Abdullah, Hayati; Saidur, R.

doi:10.1016/j.rser.2014.01.069

Cited by 825 publications

(331 citation statements)

References 62 publications

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“…Various algorithms are applied to predict user occupancy and corresponding energy requirement in order to improve system efficiency. Support vector machine and artificial neural networks-based algorithms are the two most widely used algorithms for energy prediction [13]. For instance, to improve the efficiency of a steam boiler in buildings, a data-driven scheme is proposed in [14] using artificial neural networks to achieve optimal cooling/heating.…”

Section: Related Workmentioning

confidence: 99%

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

2018

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Internet of Things (IoT) is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Trees (CART). Comparative analysis shows that our proposed model achieves 2.96% better than ANN results in terms of root mean square error metric, 6.09% better than SVM and 9.03% better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

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Section: Related Workmentioning

confidence: 99%

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

2018

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“…Spam filtering, investment risk and energy consumption forecasting are some examples of predictive modeling. Predictive modeling approaches include: Artificial Neural Networks for energy consumption [2], Support Vector Machines for energy consumption [2] and KNearest Neighbors for wind power [3].…”

Section: Related Work a Machine Learningmentioning

confidence: 99%

“…• Multi-Layer Perceptron (MLP): one of the most used techniques when evaluating machine learning models, and one of the most used for electrical consumption problems [2]. It was implemented using the Synaptic package 6 .…”

Section: A Algorithmsmentioning

confidence: 99%

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MLaaS: Machine Learning as a Service

Ribeiro

Grolinger

Capretz

2015

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

313

120

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Abstract-The demand for knowledge extraction has been increasing. With the growing amount of data being generated by global data sources (e.g., social media and mobile apps) and the popularization of context-specific data (e.g., the Internet of Things), companies and researchers need to connect all these data and extract valuable information. Machine learning has been gaining much attention in data mining, leveraging the birth of new solutions. This paper proposes an architecture to create a flexible and scalable machine learning as a service. An open source solution was implemented and presented. As a case study, a forecast of electricity demand was generated using real-world sensor and weather data by running different algorithms at the same time.

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“…NN and SV-based models appear to be the dominant approaches in consumption prediction; they have been reviewed in the work of Ahmad et al [20].…”

Section: Related Workmentioning

confidence: 99%

Energy cost forecasting for event venues

Zagar

Grolinger

Capretz

et al. 2015

2015 IEEE Electrical Power and Energy Conference (EPEC)

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Abstract-Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices. The objective is to predict the overall cost of energy consumed during an entertainment event. Predictions are carried out separately for each event category and feature selection is used to select the most effective combination of event attributes for each category. Three machine learning approaches are considered: k-nearest neighbor (KNN) regression, support vector regression (SVR) and neural networks (NN). These approaches are evaluated on a case study involving a large event venue in Southern Ontario. In terms of prediction accuracy, KNN regression achieved the lowest average error. Error rates varied greatly among different event categories.

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A review on applications of ANN and SVM for building electrical energy consumption forecasting

Cited by 825 publications

References 62 publications

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

MLaaS: Machine Learning as a Service

Energy cost forecasting for event venues

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