919 research outputs found

    Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble

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    One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.Comment: https://www.climatechange.ai/papers/neurips2022/4

    Deep Learning for Electricity Forecasting Using Time Series Data

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    The complexity and nonlinearities of the modern power grid render traditional physical modeling and mathematical computation unrealistic. AI and predictive machine learning techniques allow for accurate and efficient system modeling and analysis. Electricity consumption forecasting is highly valuable in energy management and sustainability research. Furthermore, accurate energy forecasting can be used to optimize energy allocation. This thesis introduces Deep Learning models including the Convolutional Neural Network (CNN), the Recurrent neural network (RNN), and Long Short-Term memory (LSTM). The Hourly Usage of Energy (HUE) dataset for buildings in British Columbia is used as an example for our investigation, as the dataset contains data from residential customers of BC Hydro, a provincial power utility company. Due to the temporal dependency in time-series observation data, data preprocessing is required before a model can be created. The LSTM model is utilized to create a predictive model for electricity consumption as output. Approximately 63% of the data is used for training, and the remaining 37% is used for testing. Various LSTM parameters are tested and tuned for best performance. Our LSTM predictive model can facilitate power companies’ resource management decisions

    Power Consumption Forecasting by Hybrid Deep Architectures with Data Fusion

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    Many of the deep learning solutions for time-series forecasting reported in the literature include complex neural networks that may not be directly employed by the practitioner in the field. In this study, we demonstrate how the standard deep neural network types, convolutional neural network (CNN) and long short-term memory (LSTM) network can be applied in the field of time-series forecasting. This study consists of two parts. The first part is to compare CNN and LSTM models with classical methods like Random Forest (RF) and ARIMA for the univariate electric power consumption task. The second part is to use the best performing model from the first part in the hybrid model and perform data fusion with the newly built hybrid model for the electric power consumption forecasting task. CNN and LSTM models outperform traditional methods when their performances are evaluated on the univariate electric power consumption data of Illinois, USA. We also illustrate the use of hybrid deep learning models composed of standard CNN and LSTM for data fusion with the aim of time-series forecasting. When the hybrid models are applied to the fused data of the electric power consumption data and the multivariate weather data of Illinois, USA, the forecasting performance is improved compared to that when only univariate data is used

    Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods

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    Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited. This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data. All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data

    Data-Driven Approach to Forecast Heat Consumption of Buildings with High-Priority Weather Data

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    By increasing the penetration of renewable energies in district heating (DH), the intermittency of the supply-side increases for heating service providers. Therefore, forecasting the energy consumption of buildings is needed in order to hedge against renewable power intermittency. This paper investigates the application of data-driven approaches to forecast the heat consumption of buildings in the winter, using high-priority weather data. The residential buildings are connected to mixing loops of DH to supply space heating and hot water. The heating consumption of the building is calculated using sensor data, including inflow/outflow temperature and mass flow. Principal component analysis (PCA) is applied to determine the key weather data affecting heat energy consumption. Then, the study compares the competences of artificial neural networks (ANNs), linear regression models (LRM), and k-nearest neighbors (k-NN) in forecasting heat consumption, using informative data. Based on the PCA analysis, ambient temperature and solar irradiation are shown to be the highest priority weather data, contributing to 40.6% and 29.2% of heat energy forecasting, respectively. Furthermore, the ANN exhibits a forecasting accuracy of more than 50% higher than LRM and k-NN

    Statistical and machine learning models for critical infrastructure resilience

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    This thesis presents a data-driven approach to improving predictions of critical infrastructure behaviors. In our first approach, we explore novel data sources and time series modeling techniques to model disaster impacts on power systems through the case study of Hurricane Sandy as it impacted the state of New York. We find a correlation between Twitter data and load forecast errors, suggesting that Twitter data may provide value towards predicting impacts of disasters on infrastructure systems. Based on these findings, we then develop time series forecasting methods to predict the NYISO power system behaviors at the zonal level, utilizing Twitter and load forecast data as model inputs. In our second approach, we develop a novel, graph-based formulation of the British rail network to model the nonlinear cascading delays on the rail network. Using this formulation, we then develop machine learning approaches to predict delays in the rail network. Through experiments on real-world rail data, we find that the selected architecture provides more accurate predictions than other models due to its ability to capture both spatial and temporal dimensions of the data

    Air temperature forecasting using machine learning techniques: a review

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    Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined

    Estimating top-of-atmosphere thermal infrared radiance using MERRA-2 atmospheric data

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    Space borne thermal infrared sensors have been extensively used for environmental research as well as cross-calibration of other thermal sensing systems. Thermal infrared data from satellites such as Landsat and Terra/MODIS have limited temporal resolution (with a repeat cycle of 1 to 2 days for Terra/MODIS, and 16 days for Landsat). Thermal instruments with finer temporal resolution on geostationary satellites have limited utility for cross-calibration due to their large view angles. Reanalysis atmospheric data is available on a global spatial grid at three hour intervals making it a potential alternative to existing satellite image data. This research explores using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data product to predict top-of-atmosphere (TOA) thermal infrared radiance globally at time scales finer than available satellite data. The MERRA-2 data product provides global atmospheric data every three hours from 1980 to the present. Due to the high temporal resolution of the MERRA-2 data product, opportunities for novel research and applications are presented. While MERRA-2 has been used in renewable energy and hydrological studies, this work seeks to leverage the model to predict TOA thermal radiance. Two approaches have been followed, namely physics-based approach and a supervised learning approach, using Terra/MODIS band 31 thermal infrared data as reference. The first physics-based model uses forward modeling to predict TOA thermal radiance. The second model infers the presence of clouds from the MERRA-2 atmospheric data, before applying an atmospheric radiative transfer model. The last physics-based model parameterized the previous model to minimize computation time. The second approach applied four different supervised learning algorithms to the atmospheric data. The algorithms included a linear least squares regression model, a non-linear support vector regression (SVR) model, a multi-layer perceptron (MLP), and a convolutional neural network (CNN). This research found that the multi-layer perceptron model produced the lowest error rates overall, with an RMSE of 1.22W / m2 sr um when compared to actual Terra/MODIS band 31 image data. This research further aimed to characterize the errors associated with each method so that any potential user will have the best information available should they wish to apply these methods towards their own application
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