Searching for Inflation: An Empirical Study of Real-Time Micro Behaviour Data on InflationNowcasts

This thesis introduces the application of real-time micro behaviour data in inflationnowcasts. Our study analyses if ARIMA models extended with Google search dataimproves the prediction of divisions of inflation compared to the high-performing simpleAR(1) process. This analysis addresses the issue of official inflation data containing alag of ten days. Real-time micro behaviour data can contain valuable information, whichprovides policymakers with a new tool to predict inflation in the present and near future.First, each division of inflation is assigned corresponding Google Indicators before in-samplemodel selection is performed using the Box-Jenkins Methodology. Then, comparisonsagainst ARIMA baselines are conducted to evaluate if Google search data improve modelselection. Further, out-of-sample predictions are performed for the improved divisionsfrom the preceding step. Finally, the nowcast performance for each division is comparedagainst the simple AR(1) process in terms of prediction error and ability to identify trendsand turning points.This thesis documents that Google search data improves model selection for six of twelvedivisions of inflation. These divisions consist of goods and are volatile compared tothe remaining six. Furthermore, four of six extended ARIMA models outperform thesimple AR(1) process in prediction error for the out-of-sample nowcasts. At the sametime, all divisions are improved in predicting trends and turning points. These findingssuggest that real-time micro behaviour data, represented by Google Trends, improve modelselection and nowcasts of some divisions compared to AR(1). However, when comparedto replicated and baseline ARIMA models, the only value of Google search data is inmodel selection. The improved performance is attributed to the properties of ARIMA. Toconclude, real-time data on micro behaviour are of value in model selection in inflationnowcasts.nhhma

Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital

Electrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting

Application of Fuzzy and Conventional Forecasting Techniques to Predict Energy Consumption in Buildings

This paper presents the implementation and analysis of two approaches (fuzzy and conventional). Using hourly data from buildings at the University of Granada, we have examined their electricity demand and designed a model to predict energy consumption. Our proposal was conducted with the aid of time series techniques as well as the combination of artificial neural networks and clustering algorithms. Both approaches proved to be suitable for energy modelling although nonfuzzy models provided more variability and less robustness than fuzzy ones. Despite the relatively small difference between fuzzy and nonfuzzy estimates, the results reported in this study show that the fuzzy solution may be useful to enhance and enrich energy predictions.Ministerio de Ciencia e Innovación” (Spain) (Grant PID2020-112495RB-C21MCIN/AEI/10.13039/501100011033) and from the I+D+i FEDER 2020 project B-TIC-42-UGR20 “Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía.”Next Generation EU” Margaritas Salas aids

Energy Analytics for Infrastructure: An Application to Institutional Buildings

abstract: Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework. The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to 1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques. 2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms. 3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms. With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Causality effect between electricity consumption and gross domestic product in SA and the effectiveness of the predictive techniques

A research report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Science May 23, 2017The aim of this study was to investigate the relationship and direction between electricity consumption and gross domestic product including energy infrastructure as a third variable in South Africa using the time series data from 1993 to 2015. The relationship was modelled in South Africa focusing on the industry sectors that influence economic growth and using techniques such as ARIMA model, Multivariate Regression Analysis, Vector Autoregressive and Granger Causal Test. The Vector Autoregressive model performed better than Multivariate Regression analysis in modelling the relationship between consumption and economic growth in South Africa. The Granger causal effect illustrated a direction from consumption to economic growth and again Granger cause effect from infrastructure to economic growth. The results from these models revealed that there was a relationship between electricity consumption and economic growth, as well as electricity infrastructure. South Africa supports a growth hypothesis meaning that South Africa is energy dependent. The results of the study signals that the electricity consumption of South Africa have an effect on the economic growth.MT 201

ESTIMATION OF UNBALANCE COST DUE TO DEMAND PREDICTION ERRORS USING ARTIFICIAL NEURAL NETWORK

Estimation of energy demand is used as an important tool for decision makers determining company strategies and policies. Apart from this, the fact that the actual consumption differs from the forecast is harmful for the economy of the company and even for the economy of the big scale. In this study, it is aimed to estimate the imbalance aberration caused by demand forecast deviation with Artificial Neural Networks and to evaluate its results

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects