3,155 research outputs found
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Multivariate time series forecasting is an important machine learning problem
across many domains, including predictions of solar plant energy output,
electricity consumption, and traffic jam situation. Temporal data arise in
these real-world applications often involves a mixture of long-term and
short-term patterns, for which traditional approaches such as Autoregressive
models and Gaussian Process may fail. In this paper, we proposed a novel deep
learning framework, namely Long- and Short-term Time-series network (LSTNet),
to address this open challenge. LSTNet uses the Convolution Neural Network
(CNN) and the Recurrent Neural Network (RNN) to extract short-term local
dependency patterns among variables and to discover long-term patterns for time
series trends. Furthermore, we leverage traditional autoregressive model to
tackle the scale insensitive problem of the neural network model. In our
evaluation on real-world data with complex mixtures of repetitive patterns,
LSTNet achieved significant performance improvements over that of several
state-of-the-art baseline methods. All the data and experiment codes are
available online.Comment: Accepted by SIGIR 201
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Short-Term Electricity Demand Forecasting with Machine Learning
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsAn accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant unitsâ
planning commitment. STLF reduces the overall planning uncertainty added by the intermittent
production of renewable sources; thus, it helps to minimize the hydro-thermal electricity production
costs in a power grid. Although there is some research in the field and even several research
applications, there is a continual need to improve forecasts. This project proposes a set of machine
learning (ML) models to improve the accuracy of 168 hours forecasts. The developed models employ
features from multiple sources, such as historical load, weather, and holidays. Of the five ML models
developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor
(XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based
on neural networks. Additionally, because XGBoost models are based on an ensemble of decision
trees, it facilitated the modelâs interpretation, which provided a relevant additional result, the
featuresâ importance in the forecasting
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
The increased digitalisation and monitoring of the energy system opens up
numerous opportunities to decarbonise the energy system. Applications on low
voltage, local networks, such as community energy markets and smart storage
will facilitate decarbonisation, but they will require advanced control and
management. Reliable forecasting will be a necessary component of many of these
systems to anticipate key features and uncertainties. Despite this urgent need,
there has not yet been an extensive investigation into the current
state-of-the-art of low voltage level forecasts, other than at the smart meter
level. This paper aims to provide a comprehensive overview of the landscape,
current approaches, core applications, challenges and recommendations. Another
aim of this paper is to facilitate the continued improvement and advancement in
this area. To this end, the paper also surveys some of the most relevant and
promising trends. It establishes an open, community-driven list of the known
low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape
Machine Learning Approaches for Traffic Flow Forecasting
Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions.
The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data.
In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used.
The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning.
The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time
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