Development of application-specific adjacency models using fuzzy cognitive map

Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause–effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology

ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning

Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them with renewable sources -- careful decisions need to be taken to ensure rapid but stable progress. To aid decision makers, we present a new tool, ElecSim, which is an open-sourced agent-based modelling framework used to examine the effect of policy on long-term investment decisions in electricity generation. ElecSim allows non-experts to rapidly prototype new ideas. Different techniques to model long-term electricity decisions are reviewed and used to motivate why agent-based models will become an important strategic tool for policy. We motivate why an open-source toolkit is required for long-term electricity planning. Actual electricity prices are compared with our model and we demonstrate that the use of a Monte-Carlo simulation in the system improves performance by $52.5\%$. Further, using ElecSim we demonstrate the effect of a carbon tax to encourage a low-carbon electricity supply. We show how a {\pounds}40 ($\$50$) per tonne of CO2 emitted would lead to 70% renewable electricity by 2050.Comment: e-Energy '19 Proceedings of the Tenth ACM International Conference on Future Energy System

Analysis of household electricity consumption behaviours: impact of domestic electricity generation

Adoption of renewable electricity generation technology such as photovoltaic (PV) systems is at early majority stage in most countries. Depending on solar capacity, applied feed-in tariff, and other factors, households exhibit different electricity consumption behaviours known as demand side management. This article presents three univariate methods to infer deliberative behavioural patterns at households with solar electricity technology. Strategies include qualitative principal component analysis (PCA), unsupervised Hebbian-based clustering, and clustering using a semi-supervised self-organizing map (SOM). The models are individually examined on 300 sample households with rooftop PV panels under gross metering. According to the experiments, the dominant behaviours are often general among most households, and therefore reveal themselves on first and second principal components. However, on the third and forth component the specific behaviours related to load-shifting and self-consumption, are observed. The Hebbian classifier differentiates between at least eight behaviours some of which indicating deliberative behaviours. More effectively, the SOM classifier allows for clear detection of self-consumption behaviour attributed to domestic electricity generation. The experiments, results, discussions, and recommendations for future work are inclusive