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Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model's precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model's predictive capabilities. Comparing the model's predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach. [ABSTRACT FROM AUTHOR]

Accurately predicting residential solar energy consumption is crucial for efficient electricity production, supply, and power dispatch. However, conventional forecasting methods often struggle to handle complex energy consumption data. In response to this challenge, this study develops a pioneering two-stage error-corrected combined forecasting model that integrates traditional linear methods, seasonal processing techniques, deep learning models, and intelligent optimization algorithms to outperform other combined forecasting methods in terms of performance. This research analyzes the combined weight values, shedding light on why the proposed model consistently outperforms its counterparts. To confirm its superiority, the proposed model and five benchmark models are rigorously tested in this paper using four evaluation metrics and a hypothesis testing method. The empirical results show that the proposed combined model performs well in terms of accuracy and stability. Notably, the average absolute percentage error of its 24-step ahead prediction is 2.9053 %, which outperforms all comparative models, both single and combined model. These results fully illustrate the advantages of the combined model and reaffirm the excellence of its prediction performance in predicting energy consumption. • A novel forecasting combined model is proposed for solar energy consumption. • The proposed combined model outperforms the five benchmark models. • The interpretation of combination weight in the combined model is elaborated. • Four experiments, hypothesis test and multi-step prediction are performed. [ABSTRACT FROM AUTHOR]

Lack of capital hindered the development of agricultural production and the residential energy consumption transition in rural areas, but, with the continuous advancement of digital finance technologies, rural digital credit eased the liquidity constraint of agricultural production, which influenced residential energy consumption. However, few studies explored the relationship between rural digital credit and residential energy consumption. Hence, the instrumental variable (IV) method, the mediation effect model and the survey data that covered 295 effective samples were used to analyze the impact of rural digital credit on residential energy consumption from the agricultural production perspective in this study. The results indicated that rural digital credit resulted in a significant decline of the consumption of traditional energy products, but impacts of rural digital credit on electricity consumption and the new-style energy products consumption were not significant. Further, rural digital credit reduced traditional energy products consumption via boosting the level of agricultural mechanization and the adoption of precision fertilizer applying technique. This paper was conducive to not only developing the green credit markets in rural areas, but also enhancing the residential energy consumption transition and sustainable development. • Digital credit resulted in a significant decline of the consumption of traditional energy products. • Impacts of digital credit on electricity consumption and the new-style energy products consumption were not significant. • Digital credit reduced traditional energy products consumption via boosting agricultural mechanization. • Digital credit reduced traditional energy products consumption by adopting precision fertilizer applying technique. [ABSTRACT FROM AUTHOR]

Energy retrofitting of residential buildings is considered a challenge to be tackled not only at building level, but also at urban scale, to give a stronger impulse for a concrete transition towards climate neutrality. This paper presents an easy-to-use analysis method based on the elaboration and scaling-up of data collected through the energy performance certificates (EPCs) to identify different urban energy zones distinguished by their energy performance index. This method is therefore conceived for better informing urban planning decisions, thus boosting more effective energy-sensitive urban planning strategies and eventually to foresee dedicated financial instruments to act in the more energy demanding areas of the city, establishing priorities for integrated strategies. This analytical method has been applied and tested in one municipality in the Emilia-Romagna region, Italy, where the recent urban planning law is pushing municipalities towards the development of urban planning strategies oriented to drastically improve the energy efficiency of the existing building stock. Results show that only a small number of areas manage to reach a good performance with fairly low levels of energy performance index, with the most critical situation found in the most central areas of the capital and in the hamlets. • Easy-to-use method for assessing energy performance of residential building at urban scale is proposed • Method based on data easily available for local authorities, like energy performance certificates (EPCs) and urban morphology • The method supports urban planners in municipalities in taking more targeted decisions for improving energy efficiency • The main output of the method is the definition of an energy performance index (EPI) for different urban contexts • EPI allows to wisely balance economic incentives, identifying areas where multi-purpose interventions are recommended [ABSTRACT FROM AUTHOR]