A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

Ferreira, Pedro; Gomes, João; Martins, I.; Ruano, A. E.

doi:10.3390/s121115750

Cited by 28 publications

(9 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Careful attention must be put on the building of the model, as a too complex ANN will easily overfit the training data. Several techniques like pruning [30], Bayesian regularization [31] or multi-objective genetic algorithm [32] can be employed to control the ANN complexity. In this work, we used the Bayesian Technique in order to automatically control the ANN complexity and therefore improve the generalization capability of the model [31].…”

Section: Bias Correction With An Artificial Neural Networkmentioning

confidence: 99%

Solar Forecasting in a Challenging Insular Context

Lauret¹,

Lorenz

David³

2016

Atmosphere

View full text Add to dashboard Cite

This paper aims at assessing the accuracy of different solar forecasting methods in the case of an insular context. Two sites of La Réunion Island, Le Tampon and Saint-Pierre, are chosen to do the benchmarking exercise. Réunion Island is a tropical island with a complex orography where cloud processes are mainly governed by local dynamics. As a consequence, Réunion Island exhibits numerous micro-climates. The two aforementioned sites are quite representative of the challenging character of solar forecasting in the case of a tropical island with complex orography. Hence, although distant from only 10 km, these two sites exhibit very different sky conditions. This work focuses on day-ahead and intra-day solar forecasting. Day-ahead solar forecasts are provided by the European Center for Medium-Range Weather Forecast (ECMWF). This organization maintains and runs the Numerical Weather Prediction (NWP) model named Integrated Forecast System (IFS). In this work, post-processing techniques are applied to refine the output of the IFS model for day-ahead forecasting. Statistical models like a recursive linear model or a nonlinear model such as an artificial neural network are used to produce the intra-day solar forecasts. It is shown that a combination of the IFS model and the neural network model further improves the accuracy of the forecasts.

show abstract

Section: Bias Correction With An Artificial Neural Networkmentioning

confidence: 99%

Solar Forecasting in a Challenging Insular Context

Lauret¹,

Lorenz

David³

2016

Atmosphere

View full text Add to dashboard Cite

show abstract

“…Statistical models can be further subdivided into linear models (persistent forecasts, using the clearness or clear sky indexes, or Fourier series expansions, AutoRegressive-Moving-Average-ARMA, AutoRegressive Integrated Moving Average-ARIMA or Classification And Regression Trees-CART models) and non-linear, typically computational intelligence-based models (neural networks, wavelet, fuzzy or evolutionary). Cloud-based models use weather satellite images or ground-based sky (GBS) images [11] to improve solar irradiance forecast. Basically, by processing previous sky images, clouds can be detected, and their motion extrapolated using motion vector fields, or sky cover indices can be obtained, and their evolution forecasted.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems

Bot

Ruano

2021

Inventions

View full text Add to dashboard Cite

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.

show abstract

“…The intelligent weather station described in this paper is a follow-up of the work described in [ 12 ]. It measures three atmospheric variables: Global solar radiation, air temperature and relative humidity.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Weather Station

Mestre

Ruano

Duarte

et al. 2015

Sensors

Self Cite

View full text Add to dashboard Cite

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

show abstract

A Neural Network Based Intelligent Predictive Sensor for Cloudiness, Solar Radiation and Air Temperature

Cited by 28 publications

References 55 publications

Solar Forecasting in a Challenging Insular Context

Solar Forecasting in a Challenging Insular Context

Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems

An Intelligent Weather Station

Contact Info

Product

Resources

About