36 research outputs found
Weather and Random Forest-based Load Profiling Approximation Models and Their Transferability across Climate Zones
This study is to provide predictive understanding of the associations of weather attributes with electricity load profiles across a variety of climate zones and seasons. Firstly, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked and screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons)
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting
Feature engineering is required to obtain better results for time series
forecasting, and decomposition is a crucial one. One decomposition approach
often cannot be used for numerous forecasting tasks since the standard time
series decomposition lacks flexibility and robustness. Traditional feature
selection relies heavily on preexisting domain knowledge, has no generic
methodology, and requires a lot of labor. However, most time series prediction
models based on deep learning typically suffer from interpretability issue, so
the "black box" results lead to a lack of confidence. To deal with the above
issues forms the motivation of the thesis. In the paper we propose TSDFNet as a
neural network with self-decomposition mechanism and an attentive feature
fusion mechanism, It abandons feature engineering as a preprocessing convention
and creatively integrates it as an internal module with the deep model. The
self-decomposition mechanism empowers TSDFNet with extensible and adaptive
decomposition capabilities for any time series, users can choose their own
basis functions to decompose the sequence into temporal and generalized spatial
dimensions. Attentive feature fusion mechanism has the ability to capture the
importance of external variables and the causality with target variables. It
can automatically suppress the unimportant features while enhancing the
effective ones, so that users do not have to struggle with feature selection.
Moreover, TSDFNet is easy to look into the "black box" of the deep neural
network by feature visualization and analyze the prediction results. We
demonstrate performance improvements over existing widely accepted models on
more than a dozen datasets, and three experiments showcase the interpretability
of TSDFNet.Comment: 10 page
Short-Term Load Forecasting Using Artificial Neural Network in Indonesia
Short-term Load Forecast (STLF) is a load forecasting that is very important to study because it determines the operating pattern of the electrical system. Forecasting errors, both positive and negative, result in considerable losses because operating costs increase and ultimately lead to waste. STLF research in Indonesia, especially the State Electricity Company (PLN Sulselrabar), has yet to be widely used. Methods mainly used are manual and conventional methods because they are considered adequate. In addition, Indonesia's geographical conditions are extensive and diverse, and the electricity system is complex. As a result, the factors affecting each country's electricity demand are different, so unique forecasting methods are needed. Artificial Neural Network (ANN) is one of the Artificial Intelligent (AI) methods widely used for STLF because it can model complex and non-linear relationships from networks. This paper aims to build an STLF forecasting model that is suitable for Indonesia's geographical conditions using several ANN models tested. Based on several ANN forecasting models, the test results obtained the best model is Model-6 with ANN architecture (9-20-1). This model has one hidden layer, 20 neurons in the hidden layer, a sigmoid logistic activation function (binary sigmoid), and a linear function. Forecasting performance values obtained mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of 430.48 MW2, 15.07 MW, and 2.81%, respectively
Deep coastal sea elements forecasting using U-Net based models
The supply and demand of energy is influenced by meteorological conditions.
The relevance of accurate weather forecasts increases as the demand for
renewable energy sources increases. The energy providers and policy makers
require weather information to make informed choices and establish optimal
plans according to the operational objectives. Due to the recent development of
deep learning techniques applied to satellite imagery, weather forecasting that
uses remote sensing data has also been the subject of major progress. The
present paper investigates multiple steps ahead frame prediction for coastal
sea elements in the Netherlands using U-Net based architectures. Hourly data
from the Copernicus observation programme spanned over a period of 2 years has
been used to train the models and make the forecasting, including seasonal
predictions. We propose a variation of the U-Net architecture and further
extend this novel model using residual connections, parallel convolutions and
asymmetric convolutions in order to introduce three additional architectures.
In particular, we show that the architecture equipped with parallel and
asymmetric convolutions as well as skip connections outperforms the other three
discussed models.Comment: 12 pages, 11 figure
Forecasting COVID-19 Cases in the Philippines Using Various Mathematical Models
Due to the rapid increase of COVID-19 infection cases in many countries such as the Philippines, efforts in forecasting daily infections have been made to better manage the pandemic and respond effectively. In this study, we considered the cumulative COVID-19 infection cases in the Philippines from 6 March 2020 to 31 July 2020, and forecasted the cases from 1â15 August 2020 using various mathematical modelsâweighted moving average, exponential smoothing, Susceptible-Exposed-Infected-Recovered (SEIR) model, Ornstein-Uhlenbeck process, Autoregressive Integrated Moving Average (ARIMA) model, and random forest. We compared the results to the actual data using traditional error metrics. Our results showed that the ARIMA (1,2,1) model had the closest forecast values to the actual data. Policymakers can use this result in determining which forecast method to use for their community to have data-based information for the preparation of their personnel and facilities.
Keywords: forecasting · epidemics · moving average · exponential smoothing · ARIMA · Ornstein-Uhlenbeck · SEIR · random fores
Seasonality effect analysis and recognition of charging behaviors of electric vehicles: A data science approach
Electric vehicles (EVs) presence in the power grid can bring about pivotal concerns regarding their energy requirements. EVs charging behaviors can be affected by several aspects including socio-economics, psychological, seasonal among others. This work proposes a case study to analyze seasonal effects on charging patterns, using a public real-world based dataset that contains information from the aggregated load of the total charging stations of Boulder, Colorado. Our approach targets to forecast and recognize EVs demand considering seasonal factors. Principal component analysis (PCA) was used to provide a visual representation of the variables and their contribution and the correlation among them. Then, twelve classification models were trained and tested to discriminate among seasons the charging load of electric vehicles. Later, a benchmark stage is presented for regression as well as for classification results. For regression models, examined through Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), the random Forest provides better prediction than quasi-Poisson model widely. However, it was observed that for large variations in electric vehiclesâ charging load, quasi-Poisson fits better than random forest. For the classification models, evaluated through Accuracy and the Area under the Curve, the Lasso and elastic-net regularized generalized linear (GLMNET) model provided the best global performance with accuracy up to 100% when evaluated on the test dataset. The results of this work offer great insights for enhancing demand response strategies that involve PEV charging regarding charging habits across seasons