Journal ArticleDOI
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond
Reads0
Chats0
TLDR
This paper introduces the GEFCom2014, a probabilistic energy forecasting competition with four tracks on load, price, wind and solar forecasting, which attracted 581 participants from 61 countries and concludes with 12 predictions for the next decade of energy forecasting.About:
This article is published in International Journal of Forecasting.The article was published on 2016-07-01. It has received 706 citations till now. The article focuses on the topics: Probabilistic forecasting & Electricity price forecasting.read more
Citations
More filters
Journal ArticleDOI
Review of photovoltaic power forecasting
J. Antonanzas,Natalia Osorio,Rodrigo Escobar,Ruben Urraca,Francisco Javier Martinez-de-Pison,F. Antonanzas-Torres +5 more
TL;DR: This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.
Journal ArticleDOI
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
Heng Shi,Minghao Xu,Ran Li +2 more
TL;DR: A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers’ load profiles into a pool of inputs and could address the over-fitting issue by increasing data diversity and volume.
Journal ArticleDOI
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
TL;DR: An application-oriented review of smart meter data analytics identifies the key application areas as load analysis, load forecasting, and load management and reviews the techniques and methodologies adopted or developed to address each application.
Online Short-term Solar Power Forecasting
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Journal ArticleDOI
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
TL;DR: In this paper, the authors conduct an application-oriented review of smart meter data analytics following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, identifying the key application areas as load analysis, load forecasting, and load management.
References
More filters
Journal ArticleDOI
Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this paper, a review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered.
Posted Content
Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this article, a review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered.
Journal ArticleDOI
Probabilistic electric load forecasting: A tutorial review
TL;DR: The need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilism load forecasting process are underlined.
Journal ArticleDOI
Online short-term solar power forecasting
TL;DR: In this paper, a two-stage method is proposed to forecast hourly values of solar power for horizons of up to 36 h. The results indicate that for forecasts up to 2 hours ahead, the most important input is the available observations of PV power, while for longer horizons numerical weather predictions (NWPs) are the more important input.
Online Short-term Solar Power Forecasting
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.