This research compares the potential environmental impacts of heat pumps with gas boilers and scenario analysis through utilising the life cycle approach. The study analyses the current situation with the baseline model and assesses future applications with Circular Economy (CE), Resource Efficiency (RE) and Limited Growth (LG) scenarios. Then, hybrid applications of low-carbon technologies and different manufacturing scenarios are investigated according to baseline and CE scenarios. Our results show that the use and manufacturing phases are responsible for 74% and 14% of all environmental impacts on average as expected. Even though the electricity mix of the UK has decarbonised substantially during the last decade, heat pumps still have higher lifetime impacts than gas boilers in all environmental categories except climate change impact. The carbon intensity of heat pumps is much lower than gas boilers with 0.111 and 0.097 kg CO₂e for air source heat pumps and ground source heat pumps, whereas the boiler stands as 0.241 kg CO₂e. Future scenarios offer significant reductions in most of the impact categories. The CE scenario has the highest potential with a 44% reduction for heat pumps and 27% for gas boilers on average. RE and LG scenarios have smaller potential than the CE scenario, relatively. However, several categories expect an increase in future scenarios such as freshwater ecotoxicity, marine ecotoxicity and metal depletion categories. High deployment of offshore wind farms will have a negative impact on these categories; therefore, a comprehensive approach through a market introduction programme should be provided at the beginning before shifting from one technology to another. The 50% Hybrid scenario results expect a reduction of 24% and 20% on average for ASHP and GSHP, respectively, in the baseline model. The reduction is much lower in the CE scenario, with only a 2% decrease for both heat pumps because of the reduction in heat demand in the future. These results emphasise that even though the importance of the use phase is significant in the baseline model, the remaining phases will play an important role to achieve Net-Zero targets in the future.

This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R², MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data.

Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistical error metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared, and Mean Square Error (MSE). The correlation matrix was utilized to demonstrate the relationship between the actual data and calculated values and the relationship between dependent and independent variables. The p-value and confidence interval analysis of statistical methods was performed to determine which method was more effective. It was observed that the minimum RMSE, MSE, and MAE statistical errors are 5.325 × 10⁻¹⁴, 28.35 × 10⁻²⁸, and 2.5 × 10⁻¹⁴, respectively. The MNN methods showed the strongest correlation between electricity demand forecasting and real data among all the applications tested.

Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines the impact of independent inputs on peak load estimation through various combinations and subsets using multilinear regression (MLR) equations. This research utilizes input data from 1980 to 2020, including import and export data, population, and gross domestic product (GDP), to forecast the instantaneous electricity peak load as the output value. The effectiveness of these techniques is evaluated based on error metrics, including mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R². The comparison extends to popular optimization methods, such as particle swarm optimization (PSO), and the newest method in the field, including dandelion optimizer (DO) and gold rush optimizer (GRO). This comparison is made against conventional machine learning methods, such as support vector regression (SVR) and artificial neural network (ANN), in terms of their prediction accuracy. The findings indicate that the ANN and GRO approaches produce the least statistical errors. Furthermore, the correlation matrix indicates a robust positive linear correlation between GDP and instantaneous peak load. The proposed model demonstrates strong predictive capabilities for estimating peak load, with ANN and GRO performing exceptionally well compared to other methods.

There is a complex link between building fabric, habitant expectation and behavior, energy consumption and actual internal conditions. Home owners exert total control of their homes and how people actually use buildings is not as how we think they do. Therefore, mapping occupants' behavior, and understanding how it relates to comfort and energy consumption is essential. To address this necessity, the paper reviews techniques and systems used to monitor occupants through time and location. Furthermore, it assesses the complexity, robustness, accuracy and performance of each method. To support this review, monitored data were collected using various methods to monitor people's activities in their home. These could be supported by fix building sensors, or/and wearable sensors. Results from these studies established that systems, using ultra wide band technology and radio-frequency identification hold the highest precision and accuracy, being a non-intrusive method in everyday domestic settings. In conclusion, gathering occupancy data may lead to better and more energy-efficient control system of indoor environment.

This paper presents a social media data analysis framework applied to multiple datasets. The method developed uses machine learning classifiers, where filtering binary classifiers based on deep bidirectional neural networks are trained on benchmark datasets of disaster responses for earthquakes and floods and extreme flood events. The classifiers consist of learning from discrete handcrafted features and fine-tuning approaches using deep bidirectional Transformer neural networks on these disaster response datasets. With the development of the multiclass classification approach, we compare the state-of-the-art results in one of the benchmark datasets containing the largest number of disaster-related categories. The multiclass classification approaches developed in this research with support vector machines provide a precision of 0.83 and 0.79 compared to Bernoulli naïve Bayes, which are 0.59 and 0.76, and multinomial naïve Bayes, which are 0.79 and 0.91, respectively. The binary classification methods based on the MDRM dataset show a higher precision with deep learning methods (DistilBERT) than BoW and TF-IDF, while in the case of UnifiedCEHMET dataset show a high performance for accuracy with the deep learning method in terms of severity, with a precision of 0.92 compared to BoW and TF-IDF method which has a precision of 0.68 and 0.70, respectively.