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Australia is a world leader in household uptake of solar photovoltaic systems. In this paper, we use household‐level data to identify economic, social, and environmental factors that influence actual uptake and the intention to install. We find that higher net wealth is generally associated with higher likelihood to install. Households that have mortgages, that spend more on electricity, and that pay higher average electricity prices are more likely to intend to install. Environmental preferences and related behaviours, property tenure, and space constraints are associated with both actual uptake and intention to install. We use data from the Survey of Income and Housing of 2015‐16 and the Household Energy Consumption Survey of 2012. [ABSTRACT FROM AUTHOR]

Active load shifting of the electricity demand unlocks a variety of benefits. Examples of such advantages include the increased stability of energy systems, reduced electricity costs and financial savings in the transmission as well as generation infrastructure. Although the technology necessary for demand response has been extensively studied for individual appliances or at the micro-grid level, evaluations of its nationwide impact are scarce. Yet governments and policy-makers require quantitative assessments in order to understand the underlying value and derive appropriate policies. For this purpose, this paper utilizes real-world data from the German-Austrian electricity market in order to calculate ex post the impact of demand response on electricity spot prices and load. As a result, we find that a 25% adoption rate of the available potential for load shifting could have decreased nationwide electricity expenses by approximately €500 million, or 6%, in 2014. At the same time, we observe that the price volatility rises under this scheme and thus impairs non-flexible electricity customers. This observation entails significant implications in terms of designing effective policies. [ABSTRACT FROM AUTHOR]

• A novel CTSGAN deep learning framework is first proposed and applied to model day-ahead electricity price scenarios. • The CTSGAN point forecasting model can eventually be transformed into a probabilistic forecasting model by enhancing the diversity of random input. • The CTSGAN probabilistic forecasting model can directly yield high-quality forecasting intervals with different coverage probabilities as a multi-objective forecasting model. • PSO-based optimal conditions selection method can increase the forecasting accuracy. Electricity prices in spot markets are volatile and can be affected by various factors, such as generation and demand, system contingencies, local weather patterns, bidding strategies of market participants, and uncertain renewable energy outputs. Because of these factors, electricity price forecasting is challenging. This paper proposes a scenario modeling approach to improve forecasting accuracy, conditioning time series generative adversarial networks on external factors. After data pre-processing and condition selection, a conditional TSGAN or CTSGAN is designed to forecast electricity prices. Wasserstein Distance, weights limitation, and RMSProp optimizer are used to ensure that the CTGAN training process is stable. By changing the dimensionality of random noise input, the point forecasting model can be transformed into a probabilistic forecasting model. For electricity price point forecasting, the proposed CTSGAN model has better accuracy and has better generalization ability than the TSGAN and other deep learning methods. For probabilistic forecasting, the proposed CTSGAN model can significantly improve the continuously ranked probability score and Winkler score. The effectiveness and superiority of the proposed CTSGAN forecasting model are verified by case studies. [ABSTRACT FROM AUTHOR]

Electricity market management is of great importance for cleaner production in the development of society. However, despite this significance, electricity price forecasting remains a challenging task. Hybrid models are widely employed for forecasting electricity price, which has the characteristics of being non-stationarity, random, and non-linear. Despite their success, current hybrid models require improvement. In particular, data preprocessing, artificial intelligence optimization, feature selection, and basic forecasting engine selection should be considered. In this study, in addition to these issues, we consider the negative influence of outliers on the modeling of electricity price. In particular, a novel outlier-robust hybrid model is developed for forecasting electricity price, which combines a basic forecasting engine called outlier-robust extreme learning machine model and three new algorithms. Specifically, a new optimizer called chaotic sine cosine algorithm is developed to obtain the ideal parameters for phase space reconstruction, and then a novel feature selection method is developed to construct the optimal features in the modeling of electricity price. Moreover, an effective data preprocessing method is proposed for effective forecasting by capturing electricity price features. Subsequently, experiments based on electricity price data from the electricity markets of Australia and Singapore demonstrate that the proposed model is superior to other benchmark models. Further, the model can be a reliable forecasting method not only in electricity market management, but also in modeling time series with complex nonlinear characteristics and outliers. • Outlier detection and analysis is performed based on the Isolation Forest algorithm. • An outlier-robust hybrid electricity price forecasting model is developed. • An improved data preprocessing method is proposed for optimal decomposition. • A feature selection method is proposed for optimal input features. • A chaotic sine cosine algorithm is developed for global optimization. [ABSTRACT FROM AUTHOR]