Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

Fekri, Mohammad Navid; Patel, H. R.; Grolinger, Katarina; Sharma, Vinay

doi:10.1016/j.apenergy.2020.116177

Cited by 192 publications

(89 citation statements)

References 15 publications

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“…The VMD performs the series's decomposition into several intrinsic mode functions (IMFS), while the GSA performs a feature selection in the time series. Furthermore, considering the importance of electricity load forecasting in electric systems, this task can also be performed in individual households through the employment of smart metering technologies [45,46]. In this way, Li et al [47] employed a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features to improve forecasting accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In this way, Li et al [47] employed a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features to improve forecasting accuracy. Fekri et al [46] used deep learning models based on online adaptive recurrent neural networks, considering that energy consumption patterns may change over time. In addition, several load forecasting applications have been addressed, such as peak alert systems [48], where a modified support vector regression is employed, using smart meter data and weather data as input.…”

Section: Related Workmentioning

confidence: 99%

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Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

et al. 2021

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The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

et al. 2021

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“…Both these works show very promising results, but Deep Learning methods are too computationally intensive to be adopted at the edge by resource-constrained devices. Instead, in [41] the authors propose an online adaptive method, able to continuously learn from newly arriving data and adapt to new patterns.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Savi

Olivadese

2021

IEEE Access

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Residential short-term energy consumption forecasting plays an essential role in modern decentralized power systems. The rise of innovative prediction methods able to handle the high volatility of users' electrical load has posed the basis to accomplish this task. However these methods, which mostly rely on Artificial Neural Networks, require that a huge amount of users' fine-grained sensitive consumption data are centrally collected to train a generalized forecasting model, with implications on privacy and scalability. This paper proposes an innovative architecture specifically designed to overcome this need. By exploiting Federated Learning and Edge Computing capabilities, many Long Short-Term Memory (LSTM) models are locally trained by different users based on their own historical energy consumption samples. Such models are then aggregated by a specific-purpose node to build a generalized model that is re-distributed for improved forecasting at the edge. For better forecasting, our proposed local training procedure takes as input relevant features related to calendar (i.e., hour, weekday and average consumption of previous days) and weather conditions (i.e., clustered apparent temperature), and the architecture can group users according to consumption similarities (using K-means) or socioeconomic affinities. We thoroughly evaluate the approach through simulations, showing that it can lead to similar forecasting performance than a stateof-the-art centralized solution in terms of Root Mean Square Error (RMSE), but with up to an order of magnitude lower training time and up to 50 times less exchanged data when samples are recorded at finer granularity than one hour. Nonetheless, it keeps sensitive data local and therefore guarantees users' privacy.

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“…5(a). The recursive processing of RNN contains hidden layers to with feedback loop to provide a useful information about the past states [43]. For a sequence of input x t = (x 1 , .…”

Section: B Rnnsmentioning

confidence: 99%

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

et al. 2021

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The current electric power system witnesses a significant transition into Smart Grids (SG) as a promising landscape for high grid reliability and efficient energy management. This ongoing transition undergoes rapid changes, requiring a plethora of advanced methodologies to process the big data generated by various units. In this context, SG stands tied very closely to Deep Learning (DL) as an emerging technology for creating a more decentralized and intelligent energy paradigm while integrating high intelligence in supervisory and operational decision-making. Motivated by the outstanding success of DL-based prediction methods, this article attempts to provide a thorough review from a broad perspective on the state-of-the-art advances of DL in SG systems. Firstly, a bibliometric analysis has been conducted to categorize this review's methodology. Further, we taxonomically delve into the mechanism behind some of the trending DL algorithms. We then showcase the DL enabling technologies in SG, such as federated learning, edge intelligence, and distributed computing. Finally, challenges and research frontiers are provided to serve as guidelines for future work in the futuristic power grid domain. This study's core objective is to foster the synergy between these two fields for decision-makers and researchers to accelerate DL's practical deployment for SG systems. INDEX TERMSSmart grid, deep learning, deep neural networks, edge computing, distributed and federated learning, power systems. NOMENCLATURE Abbreviations DDL Distributed deep learning DL Deep learning DRL Deep reinforcement learning DRN Deep residual network EI Edge intelligence EPS Electric power systems FL Federated learning IoT Internet of things LSTM Long short-term memory neural network The associate editor coordinating the review of this manuscript and approving it for publication was Shadi Alawneh . NN Neural network PVPF Photovoltaic power forecasting D. RESEARCH METHODOLOGY AND SYSTEMATIC REVIEW PROTOCOLStarting from September 2019, the multiple-methods approach was conducted [24]. The collection of the mainstream research papers on SG/AI from Web of Science (WoS), Scopus, IEEE Xplore, Science Direct, and Google scholar was conducted as the largest databases of peerreviewed articles. Only peer-reviewed articles written in English, providing experimental results, and having a unique identifier from the mentioned databases were taken into consideration, including reviews, research articles, patent reports, and conference proceedings. The adopted methodology for conducting this review article employs a combination of keywords categorized into three main groups, specifically, 'Deep Learning', 'Smart Grid', and 'Prediction'. The search methodology focuses on the recent research articles from 2015-2020 to identify the comprehensive statues of the AI applications on SG. The filtering process results in 220 research papers from 600 related papers selected based on their relevance by reading the title, abstract, conclusion,

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Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

Cited by 192 publications

References 15 publications

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Deep Learning in Smart Grid Technology: A Review of Recent Advancements and Future Prospects

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