A novel hybrid prediction model for aggregated loads of buildings by considering the electric vehicles

Min, Duan; Darvishan, Ayda; Mohammaditab, Rasoul; Wakil, Karzan; Abedinia, Oveis

doi:10.1016/j.scs.2018.05.009

Cited by 67 publications

(20 citation statements)

References 25 publications

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“…With artificial intelligence (AI), industrial forecasting problems can be addressed adequately, where the artificial neural network (ANN) and the support vector machine (SVM) are well-known for handling non-linear forecasting problems [23,24]. Although ANN requires considerable training time and resources to build the neural network, the flexibility and adaptability in implementing ANN are relatively high in the various application domains.…”

Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

et al. 2020

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The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

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Section: Literature Reviewmentioning

confidence: 99%

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

et al. 2020

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“…Some studies have applied deep learning to better deal with the non‐linear mapping problem in load forecasting [25–28]. To avoid the over‐fitting and under‐fitting problems in machine learning, authors in references [15, 16, 19, 21] made contributions on optimising the hyperparameters of the learning model. There are other studies that employed different methods to generate multiple outputs for one load including predict and aggregate them to get a higher accuracy [13, 14, 18, 28].…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [20], a feature selection method based on information-theoretic criteria was utilized to get the non-linearities and interacting features which improves the forecasting model by modeling interaction, relevancy and redundancy. To create a forecasting model with high accuracy, Reference [21] provided a hybrid prediction model based on a feature selection method which chooses the best candidate inputs and enhanced support vector machine which fine tunes the free parameter of the forecast engine to tackle the prediction of aggregated loads of buildings and the impact of electric vehicle. Another method based on non-linear optimization was introduced in Reference [22] which supports the assessment of load forecasts in local energy markets (LEM) simulations by generating erroneous load profiles and decreases the implications of forecast errors.…”

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confidence: 99%

“…To demonstrate the contributions of the proposed method as compared to other state-of-the-art models, the function comparisons are summarized in Table 1. It is shown that some researchers focused on the feature selection which aims to find the good input for forecasting model to improve its ability [15,[20][21][22]27]. Some studies have applied deep learning to better deal with the non-linear mapping problem in load forecasting [25][26][27][28].…”

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confidence: 99%

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Load forecasting based on deep neural network and historical data augmentation

Lai

Wang

et al. 2020

IET Generation, Transmission & Distribution

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Load forecasting is a complex non-linear problem with high volatility and uncertainty. This paper presents a novel load forecasting method known as deep neural network and historical data augmentation (DNN-HDA). The method utilizes HDA to enhance regression by DNN for monthly load forecasting, considering that the historical data to have a high correlation with the corresponding predicted data. To make the best use of the historical data, one year's historical data is combined with the basic features to construct the input vector for a predicted load. In this way, if there is C years' historical data, one predicted load can have C input vectors to create the same number of samples. DNN-HDA increases the number of training samples and enhances the generalization of the model to reduce the forecasting error. The proposed method is tested on daily peak loads from 2006 to 2015 of Austria, Czech and Italy. Comparisons are made between the proposed method and several state-of-the-art models. DNN-HDA outperforms DNN by 44%, 38% and 63% on the three datasets, respectively.

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“…Some of the auxiliary services, which can be provided by the EVs include frequency regulation, peak shaving, improved voltage profile and power quality [5]. The authors in [6] employed a demand–response management in order to solve the problems under consideration.…”

Section: Introductionmentioning

confidence: 99%

Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm

Abedinia

Bagheri

et al. 2020

IET Smart Grid

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A novel hybrid prediction model for aggregated loads of buildings by considering the electric vehicles

Cited by 67 publications

References 25 publications

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

Load forecasting based on deep neural network and historical data augmentation

Smart load scheduling strategy utilising optimal charging of electric vehicles in power grids based on an optimisation algorithm

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