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In the context of a resilient energy system, accurate residential load forecasting has become a non-trivial requirement for ensuring effective management and planning strategy/policy development. Due to the highly stochastic nature of energy load profiles, it is difficult to predict accurately, and usually, predictions are error-prone. This paper explores the potential of Empirical Mode Decomposition (EMD) in simplifying the dynamics of complex demand profiles. The simplified components are then embedded within a deep learning model, specifically Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), to forecast short-term residential loads. The novel modelling framework integrates Bayesian optimisation strategy, feature decomposition technique, feature engineering phase, and percentile-based bias correction algorithm to enhance model accuracy. The model is developed using a case-study residential dwelling located in Fintry (Scotland), and the model performance is assessed over four forecast horizons. The overall efficiency of framework is also investigated for three algorithms: random forest, gradient boosting decision trees (GBDT), and an LSTM network. While EMD and feature engineering were found to greatly improve prediction accuracy, the number of IMFs used was shown to significantly impact the model's performance and computational complexity. The model was tested on two further case studies from Fintry. • A novel forecasting framework is proposed for short-term energy demand prediction. • Multi-domain features are extracted to increase the accuracy. • Utilising empirical mode decomposition to enhance framework performance. • Exhibit strong forecasting capability without the additional computational cost. • Excellent forecasting accuracy and model robustness are validated using three households. [ABSTRACT FROM AUTHOR]

Abstract: Climate change could substantially impact the performance of the buildings in providing thermal comfort to occupants. Recently launched UK climate projections (UKCP09), clearly indicate that all areas of the UK will get warmer in future with the possibility of more frequent and severe extreme events, such as heat waves. This study, as part of the Low Carbon Futures (LCF) Project, explores the consequent risk of overheating and the vulnerability of a building to extreme events. A simple statistical model proposed by the LCF project elsewhere has been employed to emulate the outputs of the dynamic building simulator (ESP-r) which cannot feasibly be used itself with thousands of available probabilistic climate database. Impact of climate change on the daily external and internal temperature profiles has been illustrated by means of 3D plots over the entire overheating period (May–October) and over 3000 equally probable future climates. Frequency of extreme heat events in changing climate and its impact on overheating issues for a virtual case study domestic house has been analyzed. Results are presented relative to a baseline climate (1961–1990) for three future timelines (2030s, 2050s, and 2080s) and three emission scenarios (Low, Medium, and High). [Copyright &y& Elsevier]

Abstract: The effect of projected climate change on building performance is currently a growing research area. Building designers and architects are becoming more concerned that buildings designed for the current climate might not provide adequate working and living environments in the coming decades. Advice is needed to guide how existing buildings might be adapted to cope with this future climate, as well as guidance for new building design to reduce the chances of the building failing in the future. The Low Carbon Futures Project, as part of the Adaptation and Resilience to Climate Change (ARCC) programme in the UK, is looking at methods of integrating the latest climate projections from the UK Climate Impact Programme (UKCIP) into building simulation procedures. The main obstacle to this objective is that these projections are probabilistic in nature; potentially thousands of equally-probably climate-years can be constructed that describe just a single scenario. The project is therefore developing a surrogate procedure that will use regression techniques to assimilate this breadth of climate information into the building simulation process. [Copyright &y& Elsevier]

Integrating renewable energy technologies into a decentralised smart grid presents the 'Duck Curve' challenge — the disparity between peak demand and solar photovoltaic (PV) yield. Smart grid operators still lack an effective solution to this problem, resulting in the need to maintain standby fossil fuel-fired plants. The COVID-19 pandemic-induced lockdowns necessitated a shift to remote work (work-from-home) and home-based education. The primary objective of this study was to explore mitigating strategies for the duck curve challenge by investigating this notable shift in behaviour by examining the effect of remote work and education on grid and decentralised solar PV electricity use in 100 households with battery energy storage in the southwest of the UK. This study examined 1-min granular grid electricity and decentralised solar energy consumption data for April–August 2019 and 2020. The findings revealed statistically significant disparities in energy demand. Notably, there was a 1.4—10% decrease in average electricity consumption from April to August 2020 (during and following the lockdown) compared to the corresponding months of 2019. Furthermore, household grid electricity consumption was reduced by 24—25%, while self-consumption from solar PV systems increased by 7—8% during the lockdown in April and May 2020 compared to 2019. This increase in self-consumption was particularly prominent in the morning and afternoon, possibly attributed to the growing prevalence of work-from-home and home-based education. The dynamic shifts in energy consumption patterns emphasised the role of decentralised solar PV energy in meeting the evolving needs of households during unprecedented societal changes. Additionally, remote work might unlock decentralised solar PV's potential in resolving the 'Duck Curve', urging further investigation into the implications for energy infrastructure and policy development. • Analysed 1-minutely grid and decentralised solar PV energy demand data from 100 houses in a southwestern UK city. • Average electricity consumption decreased by 1.4—10% in April—August 2020 compared to 2019. • Grid electricity consumption was reduced by 24—25%, and from solar PV self-consumption increased by 7—8%. • Increased solar PV self-consumption was prominent in the morning and afternoon. • Might unlock solar PV's potential of resolving the 'Duck Curve.' [ABSTRACT FROM AUTHOR]

Abstract Rivers are among the ecosystems most sensitive to climate change. Whilst methods quantifying the impact and uncertainty of climate change on flow regime are well-established, the impact on hydroecological response is not well understood. Typically, investigative methods are qualitative in nature or follow quantitative methods of limited scope, whilst the effect of uncertainty is frequently minimised. This paper proposes a coupled hydrological and hydroecological modelling framework to assess the impact of climate change on hydroecological response quantitatively. The characterisation and reduction of modelling uncertainties was critical to the development of the framework. The ability of the framework is illustrated through application to a case study river, the River Nar, Norfolk, England, using the UKCP09 probabilistic climate projections (high emissions scenario, SRES A1F1). The results show that, by the 2050s, a reduction in instream biodiversity is virtually certain if future emissions follow the assumptions of SRES A1F1. Disruption to the natural low flow processes, essential to ecosystem functioning, is also indicated. These findings highlight the importance of the framework in water resources adaptation, particularly with respect to future environmental flows management. Highlights • We propose a modelling framework to assess hydroecological response to climate change. • Characterisation and reduction of uncertainty considered throughout. • Provides quantifiable measure of the uncertainty in the hydroecological projections. • Case study sees reduced instream biodiversity and disruption to flow processes. [ABSTRACT FROM AUTHOR]

The condition of the soil environment is critical for human health and agricultural sustainability. As a result, the environmental and ecological issues impacting the soils throughout the world are receiving more attention. This research focuses on local site-specific studies in Cerrito Blanco, Matehuala municipality, San Luis Potosi, Mexico, and describes different types of GIS interpolation techniques, multivariate statistical analysis, and various contamination indices to investigate the relationship between predictive accuracy, levels of contamination risk, and soil toxic metal elements variation. Inductively coupled plasma optical emission spectroscopy (ICP-EOS) used to test 39 digested surface soil samples for significant toxic metals (Ag, Cd, Co, Cr, Li, and Ni) after suitable dilution with deionised water. According to the results, we found that only the mean value of cadmium (Cd) exceeded the permissible standard value. After evaluating the four types of interpolation techniques, the Inverse Distance Weighting (IDW) was determined to be the optimal interpolation model for assessing the spatial distribution patterns of toxic metal concentration in the research area. The calculated contamination risk indices showed no significant high contamination risk due to soil-borne toxic metals. These results provide a comprehensive analysis of the impact of past mining activities on toxic metal concentrations in non-cultivated surface soil. • The study addresses the research of different approaches of contamination assessment techniques to identify the toxic metal contaminations in the surface soil. • The study exploits the estimation of the contamination risk level of toxic heavy metals. • The study discusses GIS interpolation techniques as well as multivariate statistical analysis which were used for spatial distribution. [ABSTRACT FROM AUTHOR]