A deployable electrical load forecasting solution for commercial buildings

Thokala, Naveen Kumar; Bapna, Aakanksha; Chandra, M. Girish

doi:10.1109/icit.2018.8352332

Cited by 19 publications

(6 citation statements)

References 15 publications

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“…They trained their models with actual data of three real commercial buildings and then tested with different time horizons. Their results showed that the SVR outperformed the NARX neural network model [20]. In another study by Koschwitz et al (2018), the accuracy of NARX RNN is assessed with different depths and is compared with SVM for heating and cooling load prediction of the non-residential buildings for all seasons.…”

Section: Previous Workmentioning

confidence: 99%

Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Shirzadi

Nizami

Khazen

et al. 2021

Designs

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Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

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Section: Previous Workmentioning

confidence: 99%

Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Shirzadi

Nizami

Khazen

et al. 2021

Designs

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“…Yuce and Rezgui [99] proposed a neural-network-based model to perform regression analysis of energy consumption within a building. Thokala [100] further considered the heterogeneity in the electrical load and proposed to use both SVM and partial RNN to forecast future load.…”

Section: ) Smart Buildingmentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field. INDEX TERMS Internet of Things, edge computing, deep learning, intelligent edge applications.

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“…Test results indicate that SVR outperforms LR and BPNN. Thokala et al [59] introduced a nonlinear autoregressive method with exogenous input-neural network (NARX-NN) and SVR to predict the office buildings energy in India. The authors concluded that SVR performs better than NARX-NN by a thin margin.…”

Section: Support Vector Machinementioning

confidence: 99%

“…According to the forecasting methods, each method whether used in the individual or hybrid level (i.e., SVM which is used in the individual level as in references [58][59] or hybrid level as in references [20][21], [25][26], [60], [76][77][78][79][80], [82]) is taken into account. All the methods are evaluated by the same way.…”

Section: Comparison Of the Previous Studiesmentioning

confidence: 99%

Electrical Load Forecasting: A methodological overview

Rostum

Zamel

Moustafa

et al. 2020

IJET

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Electric load forecasting process plays an extensive role in forecasting future electric load demand and peak load by understanding the previous data. Several researchers proved that, the presence of load forecasting error leads to an increase in operating costs. Thus Accurate electric load forecast is needed for power system security and reliability. It also improves energy efficiency, revenues for the electrical companies and reliable operation of a power system.In recent times, there are significant proliferations in the implementation of forecasting techniques. This survey aids readers to summarize and compare the latest predominant researches on electric load forecasting. Besides, it presents the most relevant studies on load forecasting over the last decade and discusses the different methods that are used in load prediction as well as the future directions in this field.

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A deployable electrical load forecasting solution for commercial buildings

Cited by 19 publications

References 15 publications

Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

Electrical Load Forecasting: A methodological overview

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