A review on renewable energy and electricity requirement forecasting models for smart grid and buildings

Ahmad, Tanveer; Zhang, Hongcai; Yan, Biao

doi:10.1016/j.scs.2020.102052

Cited by 276 publications

(97 citation statements)

References 318 publications

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“…Since the management of grids characterized by a high percentage of unpredictable renewable power generation is critical for the renewable scenario's success, solutions are required as soon as possible. For example, a first approach may focus on the improvement of the forecasting tools to predict non-programmable renewable energy production with a greater accuracy, but it was too complex to be effectively realized [10]. A second approach involves the introduction of an intermediate element between power generation and consumption, i.e., the energy storage.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Existing Barriers for the Market Development of Power to Hydrogen (P2H) in Italy

2020

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New technological solutions are required to control the impact of the increasing presence of renewable energy sources connected to the electric grid that are characterized by unpredictable production (i.e., wind and solar energy). Energy storage is becoming essential to stabilize the grid when a mismatch between production and demand occurs. Among the available solutions, Power to Hydrogen (P2H) is one of the most attractive options. However, despite the potential, many barriers currently hinder P2H market development. The literature reports general barriers and strategies to overcome them, but a specific analysis is fundamental to identifying how these barriers concretely arise in national and regional frameworks, since tailored solutions are needed to foster the development of P2H local market. The paper aims to identify and to analyze the existing barriers for P2H market uptake in Italy. The paper shows how several technical, regulatory and economic issues are still unsolved, resulting in a source of uncertainty for P2H investment. The paper also suggests possible approaches and solutions to address the Italian barriers and to support politics and decision-makers in the definition and implementation of the national hydrogen strategy.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Existing Barriers for the Market Development of Power to Hydrogen (P2H) in Italy

2020

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“…There are a large number of scale-based approaches studied for long time-series prediction. In specific, various kinds of used models, like a decision tree, linear regression, random forest, and gradient boosted trees were analyzed for different types of tasks and energy modeling analysis [35], [36]. These model's results were noted accurate, precise to the actual load demand and in this study, we have investigated the suitability of connecting this deep regression and stump tree-based algorithms into ensembles to predict the time-series big data.…”

Section: A Literature Reviewmentioning

confidence: 99%

Novel Deep Regression and Stump Tree-Based Ensemble Models for Real-Time Load Demand Planning and Management

Ahmad

Zhang

2020

IEEE Access

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The electrical power companies' load consists of several kinds of customers, for example, agricultural, building, industrial, etc. Agricultural and industrial energy consumption is based on highly inductive loads, and it leads to the electricity shutdown because of highly inductive loads effect large spikes to the power curve. These spikes show their irregular disturbance in the electric power system due to shutdown and start-up of these large loads. It is considered irregular as well as a tough job to forecast the load with the existence of such kinds of spikes. Further, the heterogeneous and multi-source load consumption characteristics necessitate the integration of evolutionary models and the ensemble techniques render better robustness and forecast accuracy. For this purpose, we proposed the novel deep regression and stump tree-based ensembles models (DRTSEM) for accurate load planning and management with the use of highly inductive load data, buildings loads, etc. The hyperparameters e.g., network weight decay, network learning rate, the total number of hidden layers and momentum of the DRTSEM is optimized by using the deep regression and stump tree-based algorithms. For forecasting analysis, two different climate zones and seven model's input model parameter selection (MPS) with new sets of the data samples are used. The forecasting performance is testified with the generalization network error and resubstituting loss, 10fold cross-validated mean square error, four performance evaluation statistics and existing tree bagger (TB) model. The mean absolute percentage error is noted 47%, 64% and 41% better than the TB model at MPS-3 to MPS-5 respectively. The proposed DRTSEM can efficiently tackle the nonlinear characteristics of the heterogeneous and multi-source loads and moreover increase accuracy and reduce the error between actual and forecasted load demand. The modeling results proved that the use of DRTSEM renders more accurate utility energy planning and management compared with the existing ensemble approaches. INDEX TERMS Novel DRTSEM model, building energy yield planning, utility energy management, multiple feature selection, deep regression, medium & long-term forecasting. NOMENCLATURE ANNs Artificial neural networks ARMA Auto regressive moving average model CDD Cooling degree-days CV Coefficient of variation CZ Climate Zone DRTSEM Deep regression and stump tree-based ensembles models DS Decision stumps ENE East-northeast The associate editor coordinating the review of this manuscript and approving it for publication was Mauro Tucci .

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“…The network was created fifty years ago and is a combination of old and new technological elements. The majority of the network's system is outdated and subject to aging due to the effects of stress, such as extreme temperatures, vibrations, water infiltration and damage due to civil engineering works [44] , [46] . Several initiatives have been implemented by the National Agency for Electricity and Water ( ONEE) to reinforce the electricity grid.…”

Section: Key Elements For Successful Transformation Of the Power Systmentioning

confidence: 99%

Towards a large-scale integration of renewable energies in Morocco

Boulakhbar

Lebrouhi

Kousksou

et al. 2020

Journal of Energy Storage

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A review on renewable energy and electricity requirement forecasting models for smart grid and buildings

Cited by 276 publications

References 318 publications

Analysis of the Existing Barriers for the Market Development of Power to Hydrogen (P2H) in Italy

Analysis of the Existing Barriers for the Market Development of Power to Hydrogen (P2H) in Italy

Novel Deep Regression and Stump Tree-Based Ensemble Models for Real-Time Load Demand Planning and Management

Towards a large-scale integration of renewable energies in Morocco

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