Automated feature engineering based on Bayesian optimization for time series

Nowadays, the forecasting time series task is relevant in solution of a wide range of problems in various spheres of human activities. One of the possible variants to provide prediction is to construct a forecasting model. The main criterion for the forecasting model quality is its accuracy. Researchers have resorted to different approaches to achieve the necessary accuracy of the forecasting model, including feature engineering. This paper presents an automated feature engineering method based on Bayesian optimization for time series data. The process of selection an optimal set of features in order to minimize the objective function is described. The developed method has an ability to create new features based on existing ones by using diverse algebraic operations. The proposed method considers any machine learning model as a black box, that allows applying different algorithms: linear regression, decision trees, neural networks, etc. The experiments demonstrated the high efficiency of the proposed approach. A comparative analysis showed that the developed algorithm in most cases was superior to human-made custom feature engineering. The accuracy of machine learning models is greatly improved with high-quality feature engineering. Mean squared error and coefficient of determination were applied to calculate quality metrics of machine learning models. Testing the developed method took place on open time series data from different subject areas (energy, manufacturing, air pollution), which provided reliable verification

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Submodular Load Clustering with Robust Principal Component Analysis

Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behaviors at different areas, highly aggregated load shapes still share similar characteristics. Load clustering is to discover such intrinsic patterns and provide useful information to other load applications, such as load forecasting and load modeling. This paper proposes an efficient submodular load clustering method for transmission-level load areas. Robust principal component analysis (R-PCA) firstly decomposes the annual load profiles into low-rank components and sparse components to extract key features. A novel submodular cluster center selection technique is then applied to determine the optimal cluster centers through constructed similarity graph. Following the selection results, load areas are efficiently assigned to different clusters for further load analysis and applications. Numerical results obtained from PJM load demonstrate the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics