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

Multistage ensemble of feedforward neural networks for prediction of heating energy consumption

Feedforward neural network models are created for prediction of heating energy consumption of a university campus. Actual measured data are used for training and testing the models. Multistage neural network ensemble is proposed for the possible improvement of prediction accuracy. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as a member of the ensemble. Three different averaging methods (simple, weighted, and median) for obtaining ensemble output are applied. Besides this conventional approach, single radial basis neural network in the second level is used to aggregate the selected ensemble members. It is shown that heating energy consumption can be predicted with better accuracy by using ensemble of neural networks than using the best trained single neural network, while the best results are achieved with multistage ensemble

Ensemble of radial basis neural networks with k-means clustering for heating energy consumption prediction

U radu je predložen i prikazan ansambl neuronskih mreža za predviđanje potrošnje toplote univerzitetskog kampusa. Za obučavanje i testiranje modela korišćeni su eksperimentalni podaci. Razmatrano je poboljšanje tačnosti predviđanja primenom k-means metode klasterizacije za generisanje obučavajućih podskupova neuronskih mreža zasnovanih na radijalnim bazisnim funkcijama. Korišćen je različit broj klastera, od 2-5. Izlazi članova ansambla su kombinovani primenom aritmetičkog, težinskog i osrednjavanja metodom medijane. Pokazano je da ansambli neuronskih mreža ostvaruju bolje rezultate predviđanja nego svaka pojedinačna mreža članica ansambla. PR Data used for this paper were gathered during study visit to NTNU, as a part of the collaborative project: Sustainable energy and environment in Western Balkans.For the prediction of heating energy consumption of university campus, neural network ensemble is proposed. Actual measured data are used for training and testing the models. Improvement of the prediction accuracy using k-means clustering for creating subsets used to train individual radial basis function neural networks is examined. Number of clusters is varying from 2 to 5. The outputs of ensemble members are aggregated using simple, weighted and median based averaging. It is shown that ensembles achieve better prediction results than the individual network

Design of ensemble forecasting models for home energy management systems

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.info:eu-repo/semantics/publishedVersio

Enhancing weather data reconstruction through hybridmethods with dimensionality reduction

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáAccurate weather analysis and forecasting rely on complete historical data. However, missing weather data often occurs due to sensor failures, data transmission issues, or limited monitoring capabilities. Reconstructing this missing data is crucial for reliableweather analysis. The Analog Ensemble (AnEn) method leverages past weather events and information from nearby stations to reconstruct and forecast data. However, incorporating nearby stations significantly increases computational costs, making the reconstruction process time consuming. To address this challenge, this dissertation integrates AnEn with dimension reduction techniques: Principal Component Analysis (PCA) and Partial Least Squares (PLS). Four hybrid methods—PCAnEn, PLSAnEn, PCClustAnEn, and PLSClustAnEn—are developed to enhance computational performance while maintaining or improving accuracy. Through four studies using three datasets, this research focuses on reconstructing six variables: wind-related variables, temperature, pressure, and humidity. The hybrid methods improved accuracy compared to the original AnEn. Notably, PLSAnEn achieves the highest reconstruction accuracy, while PLSR exhibits the fastest processing times. Additionally, PLSClustAnEn also proves to be a alternative for data reconstruction. The findings of this research contribute to the portfolio of strategies for addressing missing weather data.A análise e a previsão climática beneficiam de dados históricos completos. No entanto, é comum faltarem dados meteorológicos devido a falhas nos sensores, problemas na transmissão de dados ou limitações nas capacidades de monitoramento. A reconstrução desses dados ausentes é crucial para uma análise climática confiável. O método Analog Ensemble (AnEn) utiliza eventos meteorológicos passados e informações de estações próximas para reconstruir e prever dados. No entanto, a incorporação de estações próximas aumenta significativamente os custos computacionais, tornando o processo de reconstrução bastante demorado. Para enfrentar esse desafio, esta dissertação integra o AnEn com técnicas de redução de dimensionalidade: Análise de Componentes Principais (PCA) e Mínimos Quadrados Parciais (PLS). Quatro métodos híbridos - PCAnEn, PLSAnEn, PCClustAnEn e PLSClustAnEn - são desenvolvidos para melhorar o desempenho computacional, mantendo ou aumentando a precisão. Por meio de quatro estudos utilizando três conjuntos de dados, esta pesquisa concentrase na reconstrução de variáveis metereológicas. Os métodos híbridos aprimoraram a precisão em comparação como AnEn original. Notavelmente, o PLSAnEn alcança a maior precisão de reconstrução, enquanto o PLSR é mais eficiente em termos computacionais. Além disso, o PLSClustAnEn também se mostra uma alternativa eficiente para a reconstrução de dados. Os resultados desta pesquisa contribuem para um portfólio de estratégias de reconstrução de dados meteorológicos

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