Search

The paper contributes to the limited literature modelling the term structure of crude oil markets. We explain the term structure of crude oil prices using the dynamic Nelson–Siegel model and propose to forecast oil prices using a generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month-, 3-month-, 6-month- and 12-month-ahead forecasts obtained from a focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity. [ABSTRACT FROM AUTHOR]

Optimal automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy demand, and to improve the power resilience. In order to achieve such automatic operation, high fidelity and computationally efficiency whole building energy forecasting models are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control. However, typical black box models often require long training period and are bounded to building operation conditions during the training period. On the other hand, creating a grey box model often requires (a) long calculation time due to parameter optimization process; and (b) expert knowledge during the model development process. This paper attempts to quantitatively evaluate the impacts of two significant system characteristics: system nonlinearity and response time, on the accuracy of the model developed by a system identification process. A general methodology for building energy forecasting model development is then developed. How to adapt the system identification process based on these two characteristics is also studied. A set of comparison criteria are then proposed to evaluate the energy forecasting models generated from the adapted system identification process against other methods reported in the literature, including Resistance and Capacitance method, Support Vector Regression method, Artificial Neural Networks method, and N4SID subspace algorithm. Two commercial buildings: a small and a medium commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and evaluation. The results from this study show that the adapted system identification process is capable of significantly improve the performance of the energy forecasting model, which is more accurate and more extendable under both of the noise-free and noisy conditions than those models generated by other methods. [ABSTRACT FROM AUTHOR]

The building sector in Europe and Sweden accounts for a large part of the total European electricity end use. A large fraction of Swedish buildings are equipped with heat pumps for heating and a combination of heat pumps and decentralized energy generation from photovoltaic systems is an interesting system solution for reducing the energy use. It is important that the building has a high self-consumption of the generated PV-electricity. Self-consumption can be seen as energy conservation and has a considerable higher economic value than exported electricity for the building owner. A ground source heat pump with a novel weather forecast controller is simulated in Trnsys and compared to a reference case in regards to self-consumption and profitability. The economic analysis is based on the annuity method and a sensitivity analysis regarding annual cost, discount rate and annual electricity price change has been performed. The results indicates that the increase in self-consumed photovoltaic electricity is limited to 7% with the proposed novel weather forecast controller, which means that the controller is unprofitable. Because of this the proposed novel forecast controller is not a viable way of increasing self-consumption in systems with photovoltaic systems and ground source heat pumps in Sweden. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigate unit commitment deploying stochastic and deterministic approaches. [•] We consider day-ahead markets, its forecast and weekly price based unit commitment. [•] Stochastic and deterministic unit commitment are identical for the first planning day. [•] Unit commitment and bidding policy can be based on the deterministic approach. [•] Robust forecasting models should be estimated based on the whole planning horizon. [ABSTRACT FROM AUTHOR]