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

Electrical load forecasting models: a critical systematic review

Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest. Forecasting enables informed and efficient responses for electricity demand. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. Over 113 different case studies reported across 41 academic papers have been used for the comparison. The timeframe, inputs, outputs, scale, data sample size, error type and value have been taken into account as criteria for the comparison. The review reveals that despite the relative simplicity of all reviewed models, the regression and/or multiple regression are still widely used and efficient for long and very long-term prediction. For short and very short-term prediction, machine-learning algorithms such as artificial neural networks, support vector machines, and time series analysis (including Autoregressive Integrated Moving Average (ARIMA) and the Autoregressive Moving Average (ARMA)) are favoured. The most widely employed independent variables are the building and occupancy characteristics and environmental data, especially in the machine learning models. In many cases, time series analysis and regressions rely on electricity historical data only, without the introduction of exogenous variables. Overall, if the singularity of the different cases made the comparison difficult, some trends are clearly identifiable. Considering the large amount of use cases studied, the meta-analysis of the references led to the identification of best practices within the expert community in relation to forecasting use for electricity consumption and power load prediction. Therefore, from the findings of the meta-analysis, a taxonomy has been defined in order to help researchers make an informed decision and choose the right model for their problem (long or short term, low or high resolution, building to country level)

Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques

Scientific community is currently doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. The main challenge lies in intelligently integrating the actions of all users connected to the grid. In this context, electricity load forecasting methodologies is a key component for demand-side management. This research compares the accuracy of different Machine Learning methodologies for the hourly energy forecasting in buildings. The main goal of this work is to demonstrate the performance of these models and their scalability for different consumption profiles. We propose a hybrid methodology that combines feature selection based on entropies with soft computing and machine learning approaches, he. Fuzzy Inductive Reasoning, Random Forest and Neural Networks. They are also compared with a traditional statistical technique ARIMA (Auto Regressive Integrated Moving Average). In addition, in contrast to the general approaches where offline modelling takes considerable time, the approaches discussed in this work generate fast and reliable models, with low computational costs. These approaches could be embedded, for instance, in a second generation of smart meters, where they could generate on-site electricity forecasting of the next hours, or even trade the excess of energy.Peer ReviewedPostprint (author's final draft

Time series forecasting methodologies for electricity supply systems

Forecasting is an essential function in the electricity supply industry. Electricity demand forecasting is performed on number of different time-scales depending on the function for which they are required. In the short term (hourly) forecasts of electricity demand are required for the safe and efficient operation of the power system. Medium term forecasts (weekly) are needed for economic planning and long term (yearly) forecasts are required for deciding on system generation and transmission expansion plans. In recent years the electricity supply industry in some countries has undergone significant changes mainly due to a levelling off in the growth of electricity demand and also due to technological advances. There has been a move toward the existence of a number of smaller generating companies and the emergence of a competitors market has resulted. These changes in the structure of the industry have led to new requirements in the area of forecasting, where forecasts are now required on a small time-scale over a longer forecasting horizon, for example, the production of hourly forecasts over a period of a month. The thesis presents a novel approach to the solution of the production of short term forecasts over a relatively long term forecast horizon. The mathematical formulation of the technique is presented and an application procedure is developed. Two applications of the technique are given and the issues involved in the implementation investigated. In addition, the production of weekly electricity demand forecasts using the optimal form of the available weather variables is investigated. The value of using such a variable in cases where it is not a dominant influencing factor in the system is assessed. The application of neural networks to the problem of weekly electricity demand forecasting is examined. Neural networks are also applied to the problem of the production of both aggregate and disaggregate electricity sales forecasts for up to five years in advance. Conclusions regarding the methodologies presented in the thesis are drawn and directions for future works are considered