1,991 research outputs found
Predicting Global Irradiance Combining Forecasting Models Through Machine Learning
This paper has been presented at : 13th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2018)Predicting solar irradiance is an active research problem, with many physical models having being designed to accurately predict Global Horizontal Irradiance. However, some of the models are better at short time horizons, while others are more accurate for medium and long horizons. The aim of this research is to automatically combine the predictions of four different models (Smart Persistence, Satellite, Cloud Index Advection and Diffusion, and Solar Weather Research and Forecasting) by means of a state-of-the-art machine learning method (Extreme Gradient Boosting). With this purpose, the four models are used as inputs to the machine learning model, so that the output is an improved Global Irradiance forecast. A 2-year dataset of predictions and measures at one radiometric station in Seville has been gathered to validate the method proposed. Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons. Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.The authors are supported by the Spanish Ministry of Economy and Competitiveness, projects ENE2014-56126-C2-1-R and ENE2014-56126-C2-2-R and FEDER funds. Some of the authors are also funded by the Junta de AndalucÃa (research group TEP-220)
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and
exogenous variables, in addition to accurate prediction, it is also desired to
provide interpretable insights into the data. In this paper, we explore the
structure of LSTM recurrent neural networks to learn variable-wise hidden
states, with the aim to capture different dynamics in multi-variable time
series and distinguish the contribution of variables to the prediction. With
these variable-wise hidden states, a mixture attention mechanism is proposed to
model the generative process of the target. Then we develop associated training
methods to jointly learn network parameters, variable and temporal importance
w.r.t the prediction of the target variable. Extensive experiments on real
datasets demonstrate enhanced prediction performance by capturing the dynamics
of different variables. Meanwhile, we evaluate the interpretation results both
qualitatively and quantitatively. It exhibits the prospect as an end-to-end
framework for both forecasting and knowledge extraction over multi-variable
data.Comment: Accepted to International Conference on Machine Learning (ICML), 201
Solar Power Forecasting
Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately predict solar power. We first explored the potential of instance-based methods and proposed two new methods: the data source weighted nearest neighbour (DWkNN) and the extended Pattern Sequence Forecasting (PSF) algorithms. DWkNN uses multiple data sources and considers their importance by learning the best weights based on previous data. PSF1 and PSF2 extended the standard PSF algorithm deal with data from multiple related time series. Then, we proposed two clustering-based methods for PV power prediction: direct and pair patterns. We used clustering to partition the days into groups with similar weather characteristics and then created a separate PV power prediction model for each group. The direct clustering groups the days based on their weather profiles, while the pair patterns consider the weather type transition between two consecutive days. We also investigated ensemble methods and proposed static and dynamic ensembles of neural networks. We first proposed three strategies for creating static ensembles based on random example and feature sampling, as well as four strategies for creating dynamic ensembles by adaptively updating the weights of the ensemble members based on past performance. We then explored the use of meta-learning to further improve the performance of the dynamic ensembles. The methods proposed in this thesis can be used by PV plant and electricity market operators for decision making, improving the utilisation of the generated PV power, planning maintenance and also facilitating the large-scale integration of PV power in the electricity grid
Recommended from our members
Operational solar forecasting for the real-time market
Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning
ABSTRACT
Solar energy is now the cheapest form of electricity in history. Unfortunately,
signi.cantly increasing the electric grid’s fraction of
solar energy remains challenging due to its variability, which makes
balancing electricity’s supply and demand more di.cult. While
thermal generators’ ramp rate—the maximum rate at which they
can change their energy generation—is .nite, solar energy’s ramp
rate is essentially in.nite. Thus, accurate near-term solar forecasting,
or nowcasting, is important to provide advance warnings to
adjust thermal generator output in response to variations in solar
generation to ensure a balanced supply and demand. To address the
problem, this paper develops a general model for solar nowcasting
from abundant and readily available multispectral satellite data
using self-supervised learning.
Speci.cally, we develop deep auto-regressive models using convolutional
neural networks (CNN) and long short-term memory
networks (LSTM) that are globally trained across multiple locations
to predict raw future observations of the spatio-temporal spectral
data collected by the recently launched GOES-R series of satellites.
Our model estimates a location’s near-term future solar irradiance
based on satellite observations, which we feed to a regression model
trained on smaller site-speci.c solar data to provide near-term solar
photovoltaic (PV) forecasts that account for site-speci.c characteristics.
We evaluate our approach for di.erent coverage areas and
forecast horizons across 25 solar sites and show that it yields errors
close to that of a model using ground-truth observations
- …