Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
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doi.org/10.1109/tnnls.2013.2276053 →Countries where authors are citing Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
This map shows the geographic impact of Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals more than expected).
Fields of papers citing Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
This network shows the impact of Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals.
About Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals
This paper, published in 2013, received 507 indexed citations . Written by Hao Quan, Dipti Srinivasan and Abbas Khosravi covering the research area of Electrical and Electronic Engineering and Artificial Intelligence. It is primarily cited by scholars working on Electrical and Electronic Engineering (430 citations), Artificial Intelligence (204 citations), Management Science and Operations Research (83 citations), Control and Systems Engineering (55 citations) and Energy Engineering and Power Technology (49 citations). Published in IEEE Transactions on Neural Networks and Learning Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/tnnls.2013.2276053.