Review of Forecasting Univariate Time-series Data with Application to Water-Energy Nexus Studies & Proposal of Parallel Hybrid SARIMA-ANN Model

The necessary materials for most human activities are water and energy. Integrated analysis to accurately forecast water and energy consumption enables the implementation of efficient short and long-term resource management planning as well as expanding policy and research possibilities for the supportive infrastructure. However, the integral relationship between water and energy (water-energy nexus) poses a difficult problem for modeling. The accessibility and physical overlay of data sets related to water-energy nexus is another main issue for a reliable water-energy consumption forecast. The framework of urban metabolism (UM) uses several types of data to build a global view and highlight issues of inefficiency within the network. Failure to view the whole system contributes to the inability to comprehend the complexity and interconnectivity of the issues within the system. This complexity is found in most systems, especially with systems that must be able to support and react to vacillating human interaction and behavior. One approach to address the limitations of data accessibility and model inflexibility is through the application of univariate time-series with heterogeneous hybrid modeling addresses. Time-series forecasting uses past observations of the same variable(s) to analyze and separate the pattern from white noise to define underlying relationships to predict future behavior. There are various linear and non-linear models utilized to forecast time-series data sets; however, ground truth data sets with extreme seasonal variation are neither pure linear nor pure non-linear. This truth has propelled model building into hybrid model frameworks to combine linear and non-linear methodologies to reduce the fallacies of both model frameworks with the other\u27s strengths. This problem report works to illustrate the limitations of complex WEN studies, build a timeline of hybrid modeling analysis using univariate time-series data, and develop a parallel hybrid SARIMA-ANN model framework to increase univariate time-series analysis capabilities in order to address previously discussed WEN study limitations. The parallel Hybrid SARIMA – ANN model performs better in comparison to SARIMA, ANN, and Series hybrid SARIMA-ANN; and shows promise for research expansion with structure flexibility to expand with additional variables

Método para la Predicción de Demanda Mensual de Electricidad en Colombia utilizando Análisis Wavelet y Modelos Auto-regresivos No Lineales

This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN) of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA) with Discrete Wavelet Transform (DWT); a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR) model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing.A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in predictionEn este artículo se propone un método para la predicción mensual de la demanda en el Sistema Interconectado Nacional Eléctrico de Colombia. El método realiza preprocesamiento de la serie de tiempo utilizando un análisis multiresolución mediante tranformada wavelet discreta; se presenta un estudio para la selección de la wavelet madre y su orden, asi como del nivel de descomposición. Dado que originalmente la serie tiene comportamiento no lineal, se utilizó igualmente un modelo no lineal autoregresivo. La predicción se obtiene añadiendo a la tendencia, el estimado obtenido con el residual de la serie combinado con otros componentes extraídos durante el preproceamiento.Se incluye una revisión bibliográfica de investigaciones realizadas internacionalmente y en Colombia en relación a la aplicación de la transformada wavelet y el modelo autoregresivo no lineal a la predicción de energía eléctrica

Identificação do Movimento Linear da Série Temporal do Volume Mensal de Transações da Criptomoeda Ethereum por meio do Modelo SARIMA

This work presents the modeling of the monthly volume of transactions of the Ethereum cryptocurrency through the Box-Jenkins methodology, involving the steps: exploratory analysis, identification, guarantee and validation, some of which performed using different techniques. The model found by the SARIMA modeling (Integrated autoregressive model of seasonal moving averages) was able to demonstrate the linear behavior of the data in a satisfactory way, but it was not enough to describe the behavior of the series, composed of linear and non-linear movement, being better represented by a hybrid model.Esse trabalho apresenta a modelagem do volume mensal de transações da criptmoeda Ethereum por meio da metodologia de Box-Jenkins, envolvendo as etapas: análise exploratória, identificação, estimação e validação, algumas das quais executadas com a utilização de diferentes técnicas. O modelo encontrado pela modelagem SARIMA (Modelo autoregressivo integrado de médias móveis sazonal) conseguiu descrever o comportamento linear dos dados de forma satisfatória, mas não foi suficiente para descrever o comportamento da série, composta por movimento linear e não linear, sendo melhor representada por um modelo híbrido

Spot Price Forecasting : Evaluating the Impact of Weather Based Demand Forecasting on Electricity Market Predictions

This thesis uses electricity data sourced from Nord Pool and weather data obtained from Norsk Klimaservicesenter, seeking to forecast day-ahead spot prices by leveraging temperature-based demand forecasts. Through this analysis, we aim to examine the feasibility of developing a model that can be utilised by participants in the electricity market bidding process. A significant portion of our research efforts has been dedicated to exploring a SARIMAX model, which is widely employed in this field of research. However, we have also thoroughly examined and tested various alternative models to assess their viability by considering them as potential benchmarks. The thesis is structured into several chapters, beginning with an initial introduction that provides an overview of the electricity market in Norway. This section serves to establish the context and background for our research. Following the introduction, we delve into the presentation of the data and methods employed to address our research question. This chapter outlines the specific datasets utilised and the methodologies implemented in our analysis. Finally, we conclude the thesis by presenting our results and the implications our study might have for the participants in the Nord Pool day-ahead market. Our findings reveal a notable spurious correlation between temperature and spot price. However, we acknowledge that relying solely on weather variables is insufficient due to the influence of external factors on pricing decisions. Nevertheless, our research has yielded satisfactory results, with the best models achieving an overall error ranging between 5-10%. Our main model consistently performed well, although there were instances where alternative models outperformed it on specific days or weeks.nhhma