A combined forecasting approach with model self-adjustment for renewable generations and energy loads in smart community

Li, Yong; Wang, Zhe; Tan, Yi; Sidorov, Denis; Panasetsky, Daniil

doi:10.1016/j.energy.2017.04.032

Cited by 47 publications

(17 citation statements)

References 22 publications

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“…The potential of renewable energy generation coupled with the uncertainties related to climate change could have considerable consequences on the grid stability for example. Data-driven or machine learning approaches can be used to provide more reliable forecasting that could help the system to self-adapt [198], [199].…”

Section: Section 43: Energy Demand and Supplymentioning

confidence: 99%

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

Mauree

Naboni

Coccolo

et al. 2019

Renewable and Sustainable Energy Reviews

156

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Reviewed assessment methods for the urban environment • Critically analysed papers working on urban climate and energy demand, outdoor thermal comfort and the urban energy systems. • Demonstrated the links between the processes • An integrated workflow is proposed for assessment of the urban environment. AbstractThe current climate change is calling for a drastic reduction of energy demand as well as of greenhouse gases. Besides this, cities also need to adapt to face the challenges related to climate change. Cities, with their complex urban texture and fabric, can be represented as a diverse ecosystem that does not have a clear and defined boundary. Multiple software tools that have been developed, in recent years, for assessment of urban climate, building energy demand, the outdoor thermal comfort and the energy systems. In this review, we, however, noted that these tools often address only one or two of these urban planning aspects. There is nonetheless an intricate link between them. For instance, the outdoor comfort assessment has shown that there is a strong link between biometeorology and architecture and urban climate. Additionally, to address the challenges of the energy transition, there will be a convergence of the energy needs in the future with an energy nexus regrouping the energy demand of urban areas. It is also highlighted that the uncertainty related to future climatic data makes urban adaptation and mitigation strategies complex to implement and to design given the lack of a comprehensive framework. We thus conclude by suggesting the need for a holistic interface to take into account this multi-dimensional problem. With the help of such a platform, a positive loop in urban design can be initiated leading to the development of low carbon cities and/or with the use of blue and green infrastructure to have a positive impact on the mitigation and adaptation strategies.

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Section: Section 43: Energy Demand and Supplymentioning

confidence: 99%

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

Mauree

Naboni

Coccolo

et al. 2019

Renewable and Sustainable Energy Reviews

156

View full text Add to dashboard Cite

show abstract

“…In this context, the development of optimization methods that use nature‐inspired metaheuristic algorithms such as genetic algorithms for hybrid energy demand forecasting approaches represents an interesting future research direction for Evolutionary Computation researchers . Assuming the potentially wide applicability of the time series forecasting method and the ML methods for Big data analytics that are behind the recommendations generated by IntelliHome, the results of this study may be reasonably extrapolated to the domains of smart cities and smart grids, which increases its potential impact and interestingness among researchers from the corresponding research areas …”

Section: Introductionmentioning

confidence: 96%

“…[22][23][24] Assuming the potentially wide applicability of the time series forecasting method and the ML methods for Big data analytics that are behind the recommendations generated by IntelliHome, the results of this study may be reasonably extrapolated to the domains of smart cities and smart grids, which increases its potential impact and interestingness among researchers from the corresponding research areas. [25][26][27][28] The remainder of this paper is structured as follows: Section 2 describes the relevant literature on energy management systems for smart homes. Then, Section 3 describes the functional architecture of IntelliHome, highlighting those components aiming to optimize electrical energy consumption.…”

Section: Introductionmentioning

confidence: 99%

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

Paredes-Valverde

Alor-Hernández

Alcaráz³

et al. 2019

Computational Intelligence

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Despite there has been an increasing energy price due to factors such as supply, demand, government regulation, among others, users do not like to spend their time to analyze their power consumption and establish actions to save money. Hence, there is a need for smart solutions that help users to save energy at home in an easy way. The smart home concept is attracting the attention of both academia and industry to address this need. Nowadays, high volumes of data are available in the smart home context, facilitated by the growth of internet of things (IoT)‐based devices and advanced sensing infrastructure. Therefore, it is necessary to automatically extract useful knowledge from this information to cost‐effective use of energy at home. In this sense, this work presents IntelliHome, a smart‐home system that aims to reduce electrical energy consumption at home. To this end, IntelliHome uses big data analytics technologies and Machine Learning and statistical techniques to provide users with a meaningful perspective of their electricity consumption habits aiming to actively involve them in the energy‐saving process through real‐time information and energy‐saving recommendations. This work also discusses a case study and an evaluation aligned with the objectives of this work. The obtained results verify the effectiveness of the proposed system regarding electrical energy saving.

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“…A detailed comprehensive comparative analysis of three different ANNs in one-hour-ahead wind power prediction, including adaptive linear element, radial basis function and back-propagation network can be found in [18]. A combined forecasting approach is proposed in [19], which builds forecasting model with self-adjusting parameters of low computational complexity. The hybrid model, based on Hilbert-Huang Transform and neural networks for time series forecasting, is proposed in [20].…”

mentioning

confidence: 99%

Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model

Liu

Dreglea

2019

Energies

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Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting.

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A combined forecasting approach with model self-adjustment for renewable generations and energy loads in smart community

Cited by 47 publications

References 22 publications

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

IntelliHome: An internet of things‐based system for electrical energy saving in smart home environment

Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model

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