NN based support system for renewable energy forecasting

AbstractEnergy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect, it also helps to conserve energy for future use. Over the years, several methods for energy forecasting have been proposed, all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment. This research, however, proposes the uses of Deep Neural Network (DNN) for energy forecasting on mobile devices at the edge of the network. This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery. Nevertheless, the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them. Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source (D-RES) network. Moreover, a novel grid control algorithm that uses the forecasting model to administer a well-coordinated and effective control for renewable energy sources (RESs) in the electrical network is designed. The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network. The model was trained using a dataset from a solar power generation company in Belgium (elis) and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations. The performance of each architecture was evaluated using the mean square error (MSE) and the r-square.

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Integer code series enhanced IT2 fuzzy decision support system with alpha cuts for the innovation adoption life cycle pattern recognition of renewable energy alternatives

IEEE Access ◽

10.1109/access.2021.3060109 ◽

2021 ◽

pp. 1-1

Author(s):

Yiqing Zhao ◽

Fei Cheng ◽

Serhat Yuksel ◽

Hasan Dincer

Keyword(s):

Pattern Recognition ◽

Renewable Energy ◽

Life Cycle ◽

Decision Support ◽

Support System ◽

Innovation Adoption ◽

Fuzzy Decision ◽

Life Cycle Pattern ◽

Fuzzy Decision Support System ◽

Integer Code

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Importance of implementing smart renewable energy system using heuristic neural decision support system

Sustainable Energy Technologies and Assessments ◽

10.1016/j.seta.2021.101185 ◽

2021 ◽

Vol 45 ◽

pp. 101185

Author(s):

Wei Zhu ◽

Da Jiang ◽

BalaAnand Muthu ◽

Tamizharasi G. Seetharam

Keyword(s):

Renewable Energy ◽

Decision Support ◽

Decision Support System ◽

Support System ◽

Energy System ◽

Renewable Energy System

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An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

Remote Sensing ◽

10.3390/rs12020259 ◽

2020 ◽

Vol 12 (2) ◽

pp. 259 ◽

Cited By ~ 4

Author(s):

Małgorzata Sztubecka ◽

Marta Skiba ◽

Maria Mrówczyńska ◽

Anna Bazan-Krzywoszańska

Keyword(s):

Energy Efficiency ◽

Renewable Energy ◽

Decision Support ◽

Geographic Information Systems ◽

Decision Support System ◽

Support System ◽

Urban Areas ◽

Renewable Energy Sources ◽

Decision Makers ◽

Energy Sources

Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.

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A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.6.12319 ◽

2018 ◽

Vol 7 (1.6) ◽

pp. 20 ◽

Cited By ~ 9

Author(s):

Ansari Saleh Ahmar

Keyword(s):

Renewable Energy ◽

Energy Supply ◽

Human Life ◽

Primary Energy ◽

Energy Forecasting ◽

Indicator Method ◽

New Energy ◽

Primary Energy Supply

Humans in this world are very dependent on petroleum and energy. Petroleum and other energies are a major source in supporting human life. Regarding the reduced petroleum availability, a new energy is needed to replace the role of petroleum. Nowadays, there is much renewable energy that have been discovered and used. The purpose of this research is to predict the total primary energy supply in Indonesia by using α-Sutte Indicator and ARIMA method, and comparing those four methods which are effective in predicting data. Data from the research is renewable energy (total primary energy supply) which is obtained from OECD from 1971-2015. From the research, it is found that the α-Sutte Indicator method is more suitable to predict renewable energy (total primary energy supply) data in Indonesia compared to ARIMA (0,1,0).

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