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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
Wind power forecasting using historical data and artificial neural networks modeling
One of the main parameters affecting the reliability of the renewable energy sources (RES) system, compared to the local conventional power station, is the ability to forecast the RES availability for a few hours ahead. To this end, the main objective of this work is the prognosis of the mean, maximum and minimum hourly wind power (WP) 8hours ahead. For this purpose, Artificial Neural Networks (ANN) modeling is applied. For the appropriate training of the developed ANN models hourly meteorological data are used. These data have been recorded by a meteorological mast in Tilos Island, Greece.
For the evaluation of the developed ANN forecasting models proper statistical evaluation indices are used. According to the results, the coefficient of the determination ranges from 0.285 up to 0.768 (mean hourly WP), from 0.227 up to 0.798 (maximum hourly WP) and from 0.025 up to 0.398 (minimum hourly WP). Furthermore, the proposed forecasting methodology shows that is able to give sufficient and adequate prognosis of WP by a wind turbine in a specific location 8 hours ahead. This will be a useful tool for the operator of a RES system in order to achieve a better monitoring and a better management of the whole system
Clustering: Methodology, hybrid systems, visualization, validation and implementation
Unsupervised learning is one of the most important steps of machine learning applications. Besides its ability to obtain the insight of the data distribution, unsupervised learning is used as a preprocessing step for other machine learning algorithm. This dissertation investigates the application of unsupervised learning into various types of data for many machine learning tasks such as clustering, regression and classification. The dissertation is organized into three papers. In the first paper, unsupervised learning is applied to mixed categorical and numerical feature data type to transform the data objects from the mixed type feature domain into a new sparser numerical domain. By making use of the data fusion capacity of adaptive resonance theory clustering, the approach is able to reduce the distinction between the numerical and categorical features. The second paper presents a novel method to improve the performance of wind forecast by clustering the time series of the surrounding wind mills into the similar group by using hidden Markov model clustering and using the clustering information to enhance the forecast. A fast forecast method is also introduced by using extreme learning machine which can be trained by analytic form to choose the optimal value of past samples for prediction and appropriate size of the neural network. In the third paper, unsupervised learning is used to automatically learn the feature from the dataset itself without human design of sophisticated feature extractors. The paper points out that by using unsupervised feature learning with multi-quadric radial basis function extreme learning machine the performance of the classifier is better than several other supervised learning methods. The paper further improves the speed of training the neural network by presenting an algorithm that runs parallel on GPU --Abstract, page iv
Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models based on limited input-output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. Radial basis function (RBF) networks are an important and popular network model for nonlinear system identification and dynamical modelling. A novel generalised multiscale RBF (MSRBF) network is introduced for Dst index modelling. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using an orthogonal least squares (OLS) type algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems
A novel framework for medium-term wind power prediction based on temporal attention mechanisms
Wind energy is a widely distributed, recyclable and environmentally friendly
energy source that plays an important role in mitigating global warming and
energy shortages. Wind energy's uncertainty and fluctuating nature makes grid
integration of large-scale wind energy systems challenging. Medium-term wind
power forecasts can provide an essential basis for energy dispatch, so accurate
wind power forecasts are essential. Much research has yielded excellent results
in recent years. However, many of them require additional experimentation and
analysis when applied to other data. In this paper, we propose a novel
short-term forecasting framework by tree-structured parzen estimator (TPE) and
decomposition algorithms. This framework defines the TPE-VMD-TFT method for
24-h and 48-h ahead wind power forecasting based on variational mode
decomposition (VMD) and time fusion transformer (TFT). In the Engie wind
dataset from the electricity company in France, the results show that the
proposed method significantly improves the prediction accuracy. In addition,
the proposed framework can be used to other decomposition algorithms and
require little manual work in model training
Asset Bundling for Wind Power Forecasting
The growing penetration of intermittent, renewable generation in US power
grids, especially wind and solar generation, results in increased operational
uncertainty. In that context, accurate forecasts are critical, especially for
wind generation, which exhibits large variability and is historically harder to
predict. To overcome this challenge, this work proposes a novel
Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling,
machine learning, and forecast reconciliation techniques. The BPR framework
first learns an intermediate hierarchy level (the bundles), then predicts wind
power at the asset, bundle, and fleet level, and finally reconciles all
forecasts to ensure consistency. This approach effectively introduces an
auxiliary learning task (predicting the bundle-level time series) to help the
main learning tasks. The paper also introduces new asset-bundling criteria that
capture the spatio-temporal dynamics of wind power time series. Extensive
numerical experiments are conducted on an industry-size dataset of 283 wind
farms in the MISO footprint. The experiments consider short-term and day-ahead
forecasts, and evaluates a large variety of forecasting models that include
weather predictions as covariates. The results demonstrate the benefits of BPR,
which consistently and significantly improves forecast accuracy over baselines,
especially at the fleet level
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