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【Objective】With the rapid development of the satellite Internet of Things (IoT), a large number of short-burst users are aggravating collisions and interference among users of the access network. To address this issue, several organizations and individuals have put forward some dynamic access schemes. However, for most of the proposed dynamic access schemes, it is necessary to know the exact number of future time slot access applications. At present, some load estimation schemes have been proposed in the literature, but the accuracy of these schemes is not high, and they can only achieve load estimation for current time slot.【Methods】To solve this issue, we propose a load estimation method based on the leading code state and parameter estimation. A load prediction method based on machine learning is also proposed. The load estimation method based on leading code status and parameter estimation analyzes the relationship between the probability of leading code in different states within the time slot of the satellite IoT and the number of requests for access to the current time slot. It gives the maximum likelihood parameter estimation expression and uses the maximum likelihood parameter estimation method to estimate the current time slot load. The load prediction method based on machine learning takes the estimated load value as its historical data, combining the Long and Short Term Memory (LSTM) network and the Auto Regressive Moving Average (ARMA) model to predict the future time slot load.【Results】The simulation results show that the estimated error of the load estimation method based on leading code state and parameter estimation is less than 1%. The comprehensive error of the load prediction method based on load estimation results as historical machine learning data is about 6%.【Conclusion】The predicted error of the proposed load estimation and prediction method is within the acceptable range, thus offering accurate future slot access requests for dynamic access schemes.

With the growing demand for various IoT applications, fog nodes frequently become overloaded. Fog computing requires effective load balancing to maximize resource utilization. It is essential to determine the load on host to obtain workload consolidation. Various random parameters, including CPU utilization, the number of CPU cores, RAM, memory allocated, memory available, disk I/O, and network I/O are employed to better comprehend host workloads. In the proposed work, the host's load status is detected using an ensemble approach into three categories: under-loaded, balanced and overloaded. Further, the proposed work considers three case studies and varying numbers of virtual machines (VMs) are executed with various parameter combinations. In each case study, a different number of VMs are executed in parallel on two different platforms. In the proposed study, we predicted the load on multiple hosts by employing a variety of advanced machine-learning models. To construct an ensemble model, we selected models with higher accuracy based on retrieved performance evaluation criteria. The ensemble method is applied to deal with the worst-case scenario of the model prediction. For a selected number of case studies, the Random Forest model, Ada Boost Classifier, Gradient Boost and Decision Tree models perform better than other models. These state-of-the-art predictive models are outperformed by our proposed ensemble model and achieves an improved accuracy of nearly 82% by correctly classifying hosts as overloaded, underloaded and balanced. [ABSTRACT FROM AUTHOR]

To address the environmental impact, low efficiency, and poor accuracy of existing power load prediction methods, this study innovatively proposes a power load prediction system that combines wavelet transform with digital twin technology. Compared with similar power load prediction methods, the proposed method achieved the highest power load prediction accuracy rate of 97.26%, with the lowest MAPE and RMSE being only 3.96% each. Our proposed method has good noise resistance and overcomes the disadvantage of traditional power load prediction methods that are easily affected by the environment. Moreover, the false detection rate of the load information data obtained from the power system in the Fuxin area from 2022 to 2023 was less than 5%, further verifying the reliability of the proposed method. This achievement is attributed to the powerful signal processing capabilities of the discrete wavelet transform, advanced pattern recognition and prediction capabilities of these three deep learning network algorithms, and the intelligence of digital twin technology. The combination of these three elements has brought new technological breakthroughs to the field of power load prediction. [ABSTRACT FROM AUTHOR]

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The short‐term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the rooftop with the battery management system (BMS) to enhance energy efficiency management. However, the traditional methodologies only processed the weather parameters and power demand information for short‐term load prediction, ignoring the collected data from SSM and BMS by the advanced metering infrastructures (AMI), which probably improved prediction accuracy. In this research, many accumulated data of building and SSM are collected before methodology implementation. Considering the diversities of accumulated parameters from SSM and BMS, an adaptive convolution neural network long short‐term memory (CNN‐LSTM) is proposed for hourly electrical load prediction. The CNN could extract the critical large‐scale input feature, while the LSTM could achieve better accurate forecasts. The Pearson correlation matrix is calculated for the feature selection scheme from different data units. The hyperparameter tuning is utilized for obtaining the optimized hybrid CNN‐LSTM algorithm. The K‐fold cross‐validation is employed for deep learning algorithm verification, which includes LSTM, GRU, CNN, and Bi‐LSTM methodologies. The results prove that the hybrid CNN‐LSTM achieved outperformed improvements, which are 20.57%, 29.63%, 19.06% in MSE, MAE, MAPE, and 21.24%, 22.02%, 3.82% in validating MSE, MAE, MAPE, respectively. The hybrid CNN and LSTM combined with the feature selection scheme achieve superior predicting accuracies, proving the adaptability ability for integrating into the energy management system (EMS) of the building's SSM. [ABSTRACT FROM AUTHOR]

Prediction in the power system network is very crucial as expansion is needed in the network. Several methods have been used to predict the load on a network, from short to long time load prediction, to ensure adequate planning for future use. Since the power system network is dynamic, other parameters, such as voltage and frequency prediction, are necessary for effective planning against contingencies. Also, most power systems are interconnected networks; using isolated variables to predict any part of the network tends to reduce prediction accuracy. This review analyzed different machine learning approaches used for load, frequency, and voltage prediction in power systems and proposed a machine learning predictive approach using network topology behavior as input variables to the model. The analysis of the proposed model was tested using a regression model, Decision tree regressor, and long short-term memory. The analysis results indicate that with network topology behavior as input to the model, the prediction will be more accurate than when isolated variables of a particular Bus in a network are used for prediction. This work suggests that network topology behavior data should be used for prediction in a power system network rather than the use of isolated data of a particular bus or exogenous data for prediction in a power system. Therefore, this research recommends that the accuracy of different predictive models be tested on power system parameters by hybridizing the network topology behavior dataset and the exogenous dataset.

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building's heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) load prediction is a critical task in cloud computing. Accurate VM load prediction can help to improve resource utilization, reduce costs, and improve the quality of service. For the Hybrid LSTM and AdaBoost model, a novel approach is proposed for accurate VM load prediction in cloud environments. The proposed model combines the power of LSTM and AdaBoost model, aiming to capture temporal dependencies in the VM load data and enhance prediction accuracy. The proposed model leverages LSTM to learn patterns and dynamics from historical load data, while AdaBoost is used to create an ensemble of weak regressors that collectively make load predictions. The model follows a two-step process: first, LSTM is trained on historical load data to extract informative features, and then AdaBoost is trained to combine the predictions from multiple weak regressors. The hybrid model demonstrates improved performance in VM load prediction by effectively handling non-linear relationships, temporal dependencies, and complex load patterns. The outcome of the proposed model is calculated using metrics, i.e. MAE, MAPE, MSE, RMSE, R2, and compared with existing machine learning algorithms i.e., Adaboost, KNN, SVM and deep learning algorithms i.e. LSTM, RNN. The results clearly show the superiority of the proposed hybrid approach in accurately predicting virtual machine load, enabling efficient resource allocation and management in cloud computing environments. [ABSTRACT FROM AUTHOR]

The difficulty in balancing energy supply and demand is increasing due to the growth of diversified and flexible building energy resources, particularly the rapid development of intermittent renewable energy being added into the power grid. The accuracy of building energy consumption prediction is of top priority for the electricity market management to ensure grid safety and reduce financial risks. The accuracy and speed of load prediction are fundamental prerequisites for different objectives such as long-term planning and short-term optimization of energy systems in buildings and the power grid. The past few decades have seen the impressive development of time series load forecasting models focusing on different domains and objectives. This paper presents an in-depth review and discussion of building energy prediction models. Three widely used prediction approaches, namely, building physical energy models (i.e., white box), data-driven models (i.e., black box), and hybrid models (i.e., grey box), were classified and introduced. The principles, advantages, limitations, and practical applications of each model were investigated. Based on this review, the research priorities and future directions in the domain of building energy prediction are highlighted. The conclusions drawn in this review could guide the future development of building energy prediction, and therefore facilitate the energy management and efficiency of buildings.

An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R2), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building's thermal load, with similar accuracy to that of a physics-based simulation tool. [ABSTRACT FROM AUTHOR]

In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability. • The hybrid models integrating physics-based models and data are established. • The training costs, metrics, and transferability are evaluated for hybrid models. • The hybrid models' performances in different data quantity groups are discussed. [ABSTRACT FROM AUTHOR]

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid's stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid's infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized. • Integration of attention mechanism with LSTM improves EV load forecasting accuracy. • Optimal attention-based LSTM topology selection through iterative tuning is detailed. • Energy management approaches including demand response and renewables are discussed. • Model robustness ensured via cross-validation and performance on unseen data. • Practical implications of integrating EVs and deep learning into energy systems are explained. [ABSTRACT FROM AUTHOR]

The integration of electric vehicles (EVs) into smart grids provides major issues and prospects for effective energy management. This research examines the actual utilization of machine learning models to forecast and manage EV demand in smart grids, intended to increase grid effectiveness and dependable operation. We acquire and preprocess different datasets, considering elements such as time of usage, characteristics of the environment, and user behaviors. Multiple machine learning models, combining neural networks, support vector machines, and forests that are random, are developed and rated for their projected accuracy. Our results imply that enhanced prediction algorithms may considerably raise all the level of detail of EV load forecasts. Furthermore, we recommend load management systems based on real-time forecasts to enhance energy distribution and lower peak demand. This study presents a potential of machine learning that would promote the integration of EVs into smart grids, that tie in to more capable and efficient energy systems.

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

COVID-19 non-pharmaceutical interventions (NPIs) are changing human mobility patterns; however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors.

In an effort to address the load adjustment time in the thermal and electrical load distribution of thermal power plant units, we propose an optimal load distribution method based on load prediction among multiple units in thermal power plants. The proposed method utilizes optimization by attention to fine-tune a deep convolutional long-short-term memory network (CNN-LSTM-A) model for accurately predicting the heat supply load of two 30 MW extraction back pressure units. First, the inherent relationship between the heat supply load and thermal power plant unit parameters is qualitatively analyzed, and the influencing factors of the power load are screened based on a data-driven analysis. Then, a mathematical model for load distribution optimization is established by analyzing and fitting the unit's energy consumption characteristic curves on the boiler and turbine sides. Subsequently, by using a randomly chosen operating point as an example, a genetic algorithm is used to optimize the distribution of thermal and electrical loads among the units. The results showed that the combined deep learning model has a high prediction accuracy, with a mean absolute percentage error (MAPE) of less than 1.3%. By predicting heat supply load variations, the preparedness for load adjustments is done in advance. At the same time, this helps reduce the real-time load adjustment response time while enhancing the unit load's overall competitiveness. After that, the genetic algorithm optimizes the load distribution, and the overall steam consumption rate from power generation on the turbine side is reduced by 0.488 t/MWh. Consequently, the coal consumption rate of steam generation on the boiler side decreases by 0.197 kg (coal)/t (steam). These described changes can greatly increase the power plant's revenue by CNY 6.2673 million per year. The thermal power plant used in this case study is in Zhejiang Province, China. [ABSTRACT FROM AUTHOR]

An accurate and fast building load prediction model is critically important for guiding building energy system design, optimizing operational parameters, and balancing a power grid between energy supply and demand. A physics-based simulation tool is traditionally used to provide the building load demand; however, it is constrained by its complex model development process and requirement for engineering judgments. Machine learning algorithms (i.e., data-driven models) based on big data can bridge this gap. In this study, we used the massive energy data generated by a physics-based tool (EnergyPlus) to develop three data-driven models (i.e., LightGBM, random forest (RF), and long-short term memory (LSTM)) and compared their prediction performances. The physics-based models were developed using office prototype building models as baselines, and ranges were provided for selected key input parameters. Three different input feature dimensions (i.e., six-, nine-, and fifteen-input feature selections) were investigated, aiming to meet different demands for practical applications. We found that LightGBM significantly outperforms the RF and LSTM algorithms, not only with respect to prediction accuracy but also in regard to computation cost. The best prediction results show that the coefficient of variation of the root mean squared error (CVRMSE), squared correction coefficient (R2), and computation time are 5.25%, 0.9959, and 7.0 s for LightGBM, respectively, evidently better than the values for the algorithms based on RF (18.54%, 0.9482, and 44.6 s) and LSTM (22.06%, 0.9267, and 758.8 s). The findings demonstrate that a data-driven model is able to avoid the process of establishing a complicated physics-based model for predicting a building’s thermal load, with similar accuracy to that of a physics-based simulation tool.

Server load prediction can be utilized for load-balancing and load-sharing in distributed systems. The use of machine learning (ML) algorithms for load estimation in distributed system applications can increase the availability and performance of servers. Hence, a number of machine learning algorithms have been applied thus far for server load estimation. This study focuses on increasing the performance of game servers by accurately predicting the workload of game servers in short, medium and long term prediction situations. While doing this, various machine learning techniques have been applied and the algorithms that give the best results are presented. In terms of implementation, companies using their servers and data centers can try to increase their level of satisfaction by using these algorithms. A prediction model is developed and the estimation performances of a number of fundamental ML methods i.e., Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), Support Vector Machine (SVM), Fast Large Margin (FLM), Convolutional Neural Network CNN are analyzed. The data used during the training stage is obtained by listening to the TCP/IP packet traffic and the real-data is extracted by performing an extensive analysis of the total transferred-data that includes also the payload. In the analysis phase, the goodput is considered in order to reveal exact resource requirements. Comprehensive simulations are performed under various conditions for high accuracy performance analysis. Experimental results indicate that the proposed ML-based prediction shows promising performance in terms of load prediction when compared to the common approaches present in the literature. [ABSTRACT FROM AUTHOR]

Most of today applications are built on a micro-service architecture, where a large application is divided into different functional parts that can be deployed on many containers that enable good load balancing. Container management tools need system load forecasting means to timely balance system load. It is an important problem for systems with direct streams of popular events which periodically have large splashes of a load. In this paper, we propose the load prediction method for such systems in two cases: usual and broadcasting workload. Also, we propose an architecture of adaptive infrastructure using our load forecasting method. [ABSTRACT FROM AUTHOR]

The forecasting of the thermal power demand is essential to support the development of advanced strategies for the management of local resources on the consumer side, such as heating ventilation and air conditioning (HVAC) equipment in buildings. In this paper, a novel hybrid methodology is presented for the short-term load forecasting of HVAC thermal power demand in smart buildings based on a data-driven approach. The methodology implements an estimation of the building's activity in order to improve the dynamics responsiveness and context awareness of the demand prediction system, thus improving its accuracy by taking into account the usage pattern of the building. A dedicated activity prediction model supported by a recurrent neural network is built considering this specific indicator, which is then integrated with a power demand model built with an adaptive neuro-fuzzy inference system. Since the power demand is not directly available, an estimation method is proposed, which permits the indirect monitoring of the aggregated power consumption of the terminal units. The presented methodology is validated experimentally in terms of accuracy and performance using real data from a research building, showing that the accuracy of the power prediction can be improved when using a specialized modeling structure to estimate the building's activity. [ABSTRACT FROM AUTHOR]

• Operation optimization of phase change thermal energy storage heating system. • Novel control method combining load prediction and operation optimization. • The load prediction model integrating multiple deep learning algorithms. • The operation cost of the heating season can be saved by 10%. • The matching degree of heat supply and demand can be close to 100%. Due to the high latent heat and load-shifting capacity, phase-change thermal energy storage technology is an effective way to reduce energy costs under time-of-use electricity pricing. A favorable operation strategy is essential to exploit the advantage of the phase change thermal energy storage system. Previous studies on the operation strategy lack consideration of load prediction, which could reduce the matching degree of heat supply and demand. This study proposed a control method combing load prediction and operation optimization based on an electric boiler-phase change thermal energy storage heating system. A deep learning-based heating load prediction model was built; on this basis, an operation optimization method using dynamic programming was formulated subsequently. The proposed method was applied to a case building located in Beijing, China. By applying the proposed control method, the energy costs of the system were saved by 10% compared to the original operation strategy in the whole heating season, and the matching degree of heat supply and demand could be close to 100%, demonstrating that the method can provide users with cheaper and the best thermally comfortable heating. The proposed method is capable of achieving optimal operation of a phase change thermal energy storage system in practical applications. [Display omitted] [ABSTRACT FROM AUTHOR]

• A digging load prediction method for excavators based on RBF neural network is proposed. • The method does not require prior knowledge of soil parameters and can be implemented in real-time environment. • BPNN, coupled DEM-MBD simulation, and analytical model are used for comparative study. • The proposed approach is validated by hardware-in-loop experiments. Traditional modeling methods for digging load of excavators are often computationally expensive and require prior knowledge of soil parameters, which severely limits their engineering applications. According to the digging load characteristics in typical digging tasks, this paper presents an intelligent prediction method for digging load based on radial basis function (RBF) neural networks. The recursive least-squares (RLS) algorithm is used for weights updating. Back propagation neural network (BPNN), coupled discrete element method (DEM) and multi-body dynamics (MBD) simulation, and analytical model are applied for comparative studies. The simulation results illustrate that the RBF neural network model outperforms other comparative models in terms of prediction accuracy and computational cost. The hardware-in-loop (HIL) experiments are conducted to validate the proposed approach. Experimental results demonstrate that the error in the dynamic behavior of the excavator under the predicted digging load is less than 7%. This paper lays the foundation for digging load prediction in intelligent excavators. [ABSTRACT FROM AUTHOR]

The pervasiveness of computers in everyday life has already increased and keeps increasing the available digital data both in volume and variety/disparity. This large and dynamic availability of digital data is referred to as Big Data and is very promising in bringing forward new insights and knowledge. For obtaining these insights, the proper combination and processing of the data is required. However, the dynamicity and the increasing size of data start making their handling impossible for analysts and raise many concerns on the manner in which data will be processed from now on. Towards this direction, this paper proposes a tool that processes and combines disparate data in order to create insights regarding a future network load. In particular, the tool (based on the unsupervised machine learning technique of Self-Organizing Maps) builds knowledge on the network load that is encountered with respect to the date of interest, the location, the weather, and the features of the day (e.g., weekend, bank holiday, etc.). The obtained results reveal that the tool is capable of learning the traffic pattern and thus predicting the network load that will be encountered in the near or distant future given information for the above presented parameters with small deviations (up to 0.000553 in terms of Mean Square Error). Moreover, the tool maintains only the most representative data instances and thus reduces the data storage requirements with no loss of information. [ABSTRACT FROM AUTHOR]

The energy management of combined cooling, heating and power (CCHP) system is essential for simultaneously improving its energy and economic performances. However, the conventional operation strategies are mainly logical control, which passively adapts to users' demands. This paper proposes an optimal economic energy dispatch model of the CCHP system incorporating deep learning of load predictions to fulfill active control strategy in dynamic programming. A cross linear optimization method with a half update strategy of component efficiencies is developed to solve and calculate the variable component efficiencies in the CCHP system. Compared to the genetic algorithm, the proposed method achieves better results and the convergence time is reduced by 93%. The model predictive control of the CCHP system in load predictions of an artificial neural network is combined to the dynamic programming to realize the active energy dispatch strategy. The effects of short-term prediction and long-term prediction on forecast performances and operation costs are discussed. The case study demonstrates that the ideal prediction horizon of 8 h is recommended to fully realize the active functions of energy storage devices in the CCHP system. The proposed active strategy with model predictive control reduces the operational cost by 3.66% compared to the passive control strategy. [Display omitted] • An active economic dispatch model of CCHP system with load prediction is proposed. • The cross optimization with a half update strategy is proposed to reduce time. • The variation of prediction horizon with operation performance is analyzed. • The proposed model prediction control reduces operational cost of 3.66%. • The economic influences of prediction error on operation are quantified. [ABSTRACT FROM AUTHOR]

Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Holt–Winters smoothing for trends. Our combined method can increase the prediction accuracy. In the simulation conducted, three combined prediction models were evaluated. The prediction task was performed using the R programming language. The effectiveness of the proposed algorithm was verified by using Python's PuLP library. [ABSTRACT FROM AUTHOR]