I evaluate the out-of-sample forecasting performance of five models of Chinese and Indian energy consumption. The results are mixed, but in general the auto-regressive distributed lag and unobserved components models perform the best over multiple evaluation criteria. I then use these two models...

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving...

Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid’s infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution...

In this paper we introduce HECTOR, a new and advanced long-term electricity market model that simulates market behavior bottom-up through opportunistic, variable cost-based bidding of individual power plants into auction-based national markets with international interconnection capacities....

Historical prices information has not been exhaustively exploited in forecasting the 10-minute-ahead Composite Index of the Malaysian stock market. A simple model incorporating intraday seasonality can have lower forecast errors than a random walk. Improved accuracy is achieved when time-varying...

This paper examines the economic scenario of the United States, before and after the 2012 US Presidential election by analyzing various macroeconomic variables such as GDP, Public Debt, Exchange Rate, Social Benefit Spending, Trade, Budget Deficit/ Surplus, Unemployment Rate, Inflation and...

In this paper we present, propose and examine additional membership functions as also we propose least squares with genetic algorithms optimization in order to find the optimum fuzzy membership functions parameters. More specifically, we present the tangent hyperbolic, Gaussian and Generalized...

In this paper we present a very brief description of least mean square algorithm with applications in time-series analysis of economic and financial time series. We present some numerical applications; forecasts for the Gross Domestic Product growth rate of UK and Italy, forecasts for S&P 500...

Using virtual stock markets with artificial interacting software in- vestors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By op- timizing...