In order to reliably generate electricity to meet the demands of the customer base, it is essential to match supply with demand. Short-term load forecasting is utilised in both real-time scheduling of electricity, and load-frequency control. This paper aims to improve the accuracy of load-forecasting by using machine learning techniques to predict 30 minutes ahead using smart meter data. We utilised the k-means clustering algorithm to cluster similar individual consumers and fit distinct models per cluster. Public holidays were taken into consideration for changing customer behaviour, as was periodicity of the day, week and year. We evaluated a number of approaches for predicting future energy demands including; Random Forests, Neural Networks, Long Short-Term Memory Neural Networks and Support Vector Regression models. We found that Random Forests performed best at each clustering level, and that clustering similar consumers and aggregating their predictions outperformed a single model in each case. These findings suggest that clustering smart meter data prior to forecasting is an important step in improving accuracy when using machine learning techniques.
Electricity market modelling is often used by governments, industry and agencies to explore the development of scenarios over differing timeframes. For example, how would the reduction in cost of renewable energy impact investments in gas power plants or what would be an optimum strategy for carbon tax or subsidies?Cost optimization based solutions are the dominant approach for understanding different long-term energy scenarios. However, these types of models have certain limitations such as the need to be interpreted in a normative manner, and the assumption that the electricity market remains in equilibrium throughout. Through this work, we show that agent-based models are a viable technique to simulate decentralised electricity markets. The aim of this paper is to validate an agent-based modelling framework to increase confidence in its ability to be used in policy and decision making.Our framework can model heterogeneous agents with imperfect information. The model uses a rules-based approach to approximate the underlying dynamics of a real world, decentralised electricity market. We use the UK as a case study, however, our framework is generalisable to other countries. We increase the temporal granularity of the model by selecting representative days of electricity demand and weather using a k-means clustering approach.We show that our framework can model the transition from coal to gas observed in the UK between 2013 and 2018. We are also able to simulate a future scenario to 2035 which is similar to the UK Government, Department for Business and Industrial Strategy (BEIS) projections. We show a more realistic increase in nuclear power over this time period. This is due to the fact that with current nuclear technology, electricity is generated almost instantaneously and has a low short-run marginal cost [13].
clicSAND is a user interface for OSeMOSYS users to analyse energy system models in terms of investment planning and optimal allocation of resources. The first version was only available for Windows users with at least 8 GB RAM and an installed Windows Access database to visualise the results. This paper presents an updated version of the software called clicSANDMac, which was specifically developed for macOS OSeMOSYS users and overcomes the previous limitations – as described in Section 1. The main changes to the software, its functionalities, and configuration are presented in Section 2. The paper ends with Section 3, which provides suggestions for future work and the potential impact of the clicSANDMac software.
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