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Solar energy has been rapidly utilized in urban environments owing to its significant potential to fulfill the energy demand. The precise forecasting of solar energy, including solar radiation and photovoltaic power forecasting, is crucial for effective energy utilization in cities. Currently, artificial intelligence algorithms, including machine learning (ML) and deep learning (DL) algorithms, have been widely utilized in the research of solar energy forecasting and have shown excellent performance. The different research scales lead to variations in the influencing factors and forecasting approaches used in solar energy prediction. Therefore, this paper provides a comprehensive review of solar radiation and photovoltaic power forecasting research using ML and DL algorithms from a multi-scale perspective, including the meso-scale, micro-scale, and building-scale forecasting in urban environments. The aim is to summarize the state-of-the-art progress and evaluate the solar forecasting effectiveness in diverse research scenarios. The characteristic analyses of ML- and DL-based solar forecasting approaches, prediction models, forecasting horizons, inputs and outputs were conducted for different research scales. This paper discusses the existing issues and future research directions for multiscale solar energy forecasting in cities. The analyses can help researchers and engineers in identifying suitable prediction algorithms and approaches. Consequently, this exploration can contribute to the efficient utilization of solar radiation and photovoltaic power generation in multi-scale urban environments. [Display omitted] • Machine learning and deep learning-based solar radiation and photovoltaic power forecasting research in urban environment are classified from a multi-scale perspective. • The characteristics and key points of solar radiation and photovoltaic power forecasting at the meso-scale, micro-scale, and building-scale in cities are analyzed. • Solar radiation and photovoltaic power forecasting approaches, prediction models, input and output values, and forecasting horizons are discussed. • Future research directions of solar radiation and photovoltaic power forecasting in urban environment are presented. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM 1 , PM 2.5 , PM 10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (σ). RMSE ranged between 0.65 and 1 μg/m3, while σ has values between 2.75 and 4.1 μg/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square - Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and σ vary between 8 and 9.1 μg/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 μg/m3, while the σ ranged between 4.44 and 6.26 μg/m3, further validating the precision of our forecasting model. [ABSTRACT FROM AUTHOR]

As an important means to reduce CO 2 emissions from coal-fired power plants, CCUS project has received more and more attention. In this paper, the comprehensive benefits of CCUS project in coal-fired power plants are defined as economic benefits, environmental benefits and energy benefits, and comprehensive evaluation and prediction analysis of CCUS project in coal-fired power plants are made from these three aspects. The goodness-of-fit of the constructed comprehensive evaluation model can reach 0.99, and the ratio of economic, environmental and energy benefits is about 5: 9: 1. Among them, the best scenario of policy influence, economic development and technological progress can promote CCUS project with benefits of 6.51%, 6.56% and 13.07% respectively. Through case analysis, it can be calculated that when the service life of CCUS project reaches 21.7 years, the investment cost can be recovered from the economic benefits. In the prediction model, the goodness of fit of the economic benefit prediction model is up to 0.95, in which the carbon capture cost of CCUS technology, operation and maintenance cost and carbon price fluctuation play a major role. The goodness of fit of the environmental benefit prediction model can reach 0.90, in which the carbon capture capacity of CCUS project and the world environmental situation are the main influencing factors, and the goodness of fit of the energy benefit prediction model can reach up to 0.95, which is mainly affected by the energy consumption level of CCUS project. The research results show that CCUS project of coal-fired power plants at home and abroad has a good development prospect and remarkable comprehensive benefits. This paper puts forward relevant policy suggestions that are beneficial to the development of CCUS project in coal-fired power plants, with a view to providing empirical evidence for the evaluation and decision-making of CCUS project in coal-fired power plants. • A comprehensive evaluation and prediction system of CCUS projects in coal-fired power plants is established. • In this paper, neural network is used for benefit accounting and evaluation. Meanwhile, machine learning is used for benefit prediction, with a good degree of fitting and high accuracy of the model.Neural network and machine learning are used to forecast and estimate the benefits. • The analysis method is universal and can be used to study CCUS projects in other industries. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting for the district heating network (DHN) is vital for energy companies to develop a stable and dependable generation capacity. Many machine learning methods have been developed recently, and several of them are now employed in DHN for heat demand forecasting. Despite the rapid growth of machine learning methods, developing accurate forecasting techniques for sparse DHN data remains a difficult task. As a result, hybrid solutions have been introduced, to overcome the issue. This paper develops a hybrid model to accurately determine DHN heat load utilization based on the LightGBM and FB-Prophet models. To get accurate output results from the FB-Prophet model, it is embedded with the positional encoding layers. The LightGBM model is used to train the non-linear components obtained from the output of the FB-Prophet model. The optimal parameters for the LightGBM model are evaluated, and three hyperparameter approaches are compared, namely grid search, random search, and sequential model-based optimization, and the results indicate that the grid search approach is the most accurate. The proposed model's effectiveness is compared to the performance of other 10 machine learning algorithms. The proposed hybrid model's forecasting performance is superior, with MAE of 30.88, RMSE of 50.50, and R2 of 0.92; therefore, it can perform an efficient guiding role in DHN energy distribution. • Hybrid model is built for heat load forecasting using LightGBM and FB-Prophet model. • Positional encoding layers are added to improve the output of the FB-Prophet model. • The LightGBM model is used to train the non-linear components. • Proposed hybrid model is compared with another ten machine learning models. • MAE of 30.88, RMSE of 50.50, and R2 of 0.92 proved the effectiveness of proposed model. [ABSTRACT FROM AUTHOR]

Motivated by reducing carbon emissions, carbon trading market have been opened to promote environmental protection. Accurate carbon trading volume and price forecasts have far-reaching implications for environmental and energy policy formulation. As the country with the most massive carbon emissions in the world, China's carbon price trends, carbon trading volume trends, and policy orientation are of great significance. As matters stand, there are few studies on carbon price and trading volume forecasts, and no research paper has covered carbon price and trading volume forecasts for all carbon trading markets in China. In order to provide a relatively complete reference for the carbon price and trading volume in China, this paper uses six machine learning models to predict the daily carbon price and trading volume of eight carbon markets in China, including Beijing, Shenzhen, Guangdong, Hubei, Shanghai, Fujian, Tianjin, Chongqing. The prediction models include extreme gradient boosting, random forest, kernel-based nonlinear extension of the Arps decline model optimized by grey wolf optimizer (GWO-KNEA), support vector machine optimized by particle swarm optimizer, support vector machine optimized by fruit fly optimizer and simulated annealing algorithm, and radial basis function neural network (RBFNN). Moreover, an advanced data denoising method, i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is used in the models to smooth the raw data. Because the eight carbon trading markets have different carbon price and trading volume distribution characteristics in time series, by comparing the prediction results, more suitable models for the specific carbon market can be obtained. The prediction results show that CEEMDAN-GWO-KNEA and CEEMDAN-RBFNN are more competitive in predicting carbon prices and trading volume in China because of their best performance in many datasets. The average accuracy of CEEMDAN-RBFNN and CEMDAN-GWO-KNEA in carbon price prediction is 98.40% and 97.89%, respectively. The predictive stability of each model in different application scenarios is also discussed. The results show that high prediction accuracy does not mean better prediction stability, but CEEMDAN-RBFNN and CEMDAN-GWO-KNEA can still guarantee high stability in most datasets. [ABSTRACT FROM AUTHOR]

The growing human population in this modern society hugely depends on the energy to fulfill their day-to-day needs and activities. Renewable energy sources, especially solar energy, can satisfy the global power demand while reducing global warming caused by conventional sources. Solar irradiance is an essential component in solar power applications. The availability of solar irradiance is influenced by several factors, such as forecasting horizon, weather classification, and performance evaluation metrics, which also need consideration. The accurate forecasting of solar irradiance is of utmost importance for the power system designers and grid operators for efficient management of solar energy systems. The intermittent and non-stationary nature of solar irradiance makes many existing statistical and machine learning approaches less competent in providing accurate predictions. In this context, deep learning models have been proposed by several researchers to reduce the limitations of existing machine learning models and improve prediction accuracy. In this work, an extensive and comprehensive review of deep learning-based solar irradiance forecasting models is presented. The effectiveness and efficacy of several deep learning models, including long short-term memory, deep belief network, echo state network, convolution neural network, etc. have been reviewed. The results obtained in the reported studies proved the superiority of deep learning models in solar forecasting applications. Few researchers have proposed deep hybrid models to improve the prediction performance further. A study reported that the hybrid of CNN-LSTM can enhance the prediction accuracy by 3.62%, 25.29%, 34.66%, 37.37% and 26.20% over CNN, LSTM, GRU, RNN and DNN, respectively. Overall, this paper offers preliminary guidelines for a detailed view of deep learning techniques that researchers and engineers can use to improve the solar photovoltaic plant's modeling and planning. [Display omitted] • A review of deep learning solar irradiance forecasting models is presented. • Factors that influence the accuracy of forecast models are also studied. • A comparative analysis of several deep learning models is conducted. • Hybrid deep learning models performed well over basic deep learning models. • Hybrid deep learning models are recommended for enhanced performance. [ABSTRACT FROM AUTHOR]

The Norwegian rural distributed power network is mainly designed for Holiday Cabins with limited electrical loading capacity. Load prediction analysis, within such type of network, is necessary for effective operation and to manage the increasing demand of new appliances (e. g. electric vehicles and heat pumps). In this paper, load prediction of a distributed power network (i.e. a typical Norwegian rural area power network of 125 cottages with 478 kW peak demand) is carried out using regression analysis techniques for establishing autocorrelations and correlations among weather parameters and occurrence time in the period of 2014–2018. In this study, the regression analysis for load prediction is done considering vertical and continuous time approach for day-ahead prediction. The vertical time approach uses seasonal data for training and inference, compared to continuous time approach which utilizes all data in a continuum from the start of the dataset until the time period used for inference. The vertical approach does this with even fewer data than continuous approach. The regression tools can perform using the low amount of data, and the prediction accuracy matches with other techniques. It is observed through load predictive analysis that the autocorrelation by vertical approach with kNN-regressor gives a low Symmetric Mean Absolute Percentage Error. The kNN-regressor is compared with Random Forest Regressor, and it uses autoregression. Autoregression is the simplest and the most straightforward predictive model based on the targeted vector itself. The autoregression indicates the decline and incline of the time-series, and thus gives a finite gradient for the curvature of load profile. It is observed that joint learning of regression tools with autoregression can predict time-series components of the different load profile characteristics. The presented load prediction analysis is going to be useful for distributed network operation, demand-side management, integration of renewable energy sources and distributed generators. • Smart load prediction analysis with weather parameters for Norwegian rural power network with holiday resorts. • Regression analysis for autocorrelation and correlation among weather, time and seasonal load. • Joint learning of regression tools with autoregression to predict time series components of different characteristics. [ABSTRACT FROM AUTHOR]

Solar radiation components assessment is a highly required parameter for solar energy applications. Due to the non-stationary behavior of solar radiation parameters and variety of atmosphere conditions, stand-alone forecasting models are insufficient for providing accurate estimation in some cases. In this respect, different hybrid models have been proposed in recent years to overcome the limitations of single models and boost the forecasting precision. In this paper, acomprehensive literature review of the recent trends in hybrid model techniques for solar radiation components assessment is presented. The main objective behind this study is to present a comparative study between different hybrid models, explore their application, and identify promising and potential models for solar radiation application assessment. The performance ranking of each hybrid model is complicated due the diversity of the data length and scale, forecasting horizon, performance metrics, time step and climate condition. Overall, the presented study provides preliminary guidelines for a complete view of the hybrid models and tools that can be used in order to improve solar radiation assessment. • In-depth review of different hybrid methods for solar radiation forecasting is presented. • Forecasting horizon for each category is discussed. • Worldwide regions and data are investigated. [ABSTRACT FROM AUTHOR]