Intelligent Load Forecasting for Central Air Conditioning Using an Optimized Hybrid Deep Learning Framework

The dynamic variations in connected loads and the intermittent nature of renewable energy sources significantly impact smart grid reliability. Accurate load and generation forecasting stand pivotal for enhancing grid reliability and efficient operations. In this study, a comprehensive four-step approach is introduced for short-term load forecasting (STLF) aimed at precisely estimating power demand and generation. The process unfolds with data collection, followed by rigorous standardization, preprocessing, and cleansing of demand and generation data. Subsequently, a hybrid deep learning model, comprising bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and a fully connected layer is trained using the clean data. This model harnesses the temporal dependencies within the data for accurate predictions. The model's performance is then evaluated using mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), providing forecasted values for both generation and demand on minute, hourly, daily, and weekly intervals. Notably, the proposed approach achieves a remarkable MSE of 0.0058 for load forecasting and 0.0033 for generation forecasting. Comparative analysis with state-of-the-art (SOTA) techniques in terms of accuracy and computational cost underscores the superior accuracy of the proposed framework in forecasting both generation and demand. Importantly, the proposed approach bridges the gap in reliability enhancement for smart grids operating, a facet lacking in many existing methodologies. This signifies the potential of the proposed approach to bolster smart grid reliability, ensuring more reliable operations.

Experimental and computational assessment of wetting pattern for two-layered soil profiles in pulse drip irrigation: Designing a novel optimized bidirectional deep learning paradigm

The short-term wind power prediction based on a multi-layer stacked model of BO[sbnd]CNN-BiGRU-SA

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning.

The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model’s significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements.

This research evaluates the performance of deep learning (DL) models in predicting rainfall in George Town, Penang, utilizing the open-source NASA POWER meteorological data, which includes variables such as rainfall, dew point, solar radiation, wind speed, relative humidity, and temperature. This study introduces a newly developed hybrid DL based on the integration of a 2D convolutional neural network (CNN2D) with a bidirectional recurrent neural network (BRNN) and a bidirectional gated recurrent unit (BGRU). The proposed models, CNN2D–BGRU and BRNN–BGRU, were compared against standalone models CNN2D, BRNN, and BGRU. The results indicate that the BRNN–BGRU model is the most effective, with a root mean square error (RMSE) value of 2.59, a mean absolute error (MAE) value of 1.97, a Pearson correlation coefficient (PCC) value of 0.79, and a Willmott index (WI) value of 0.88. In a 3-day prediction, the BRNN–BGRU model also performed the best, with a test WI value of 0.83, a PCC value of 0.69, a RMSE value of 3.02, and MAE value of 2.34. The hybrid BRNN–BGRU model consistently excels in predicting multi-step rainfall in tropical regions using the NASA POWER dataset. These findings can contribute to the development of advanced rainfall-predicting systems for more effective management of water resources and flooding in urban areas.

To address the challenge of information overload in the rapidly expanding culinary sector, a recommendation system using Content-Based Filtering (CBF) and the Bidirectional Gated Recurrent Unit (Bi-GRU) algorithm was developed. This system can help users to suggest culinary options based on user profiles and preferences. Twitter (X) is frequently used to gather culinary reviews in Bandung, forming the foundation for developing recommendation systems. This research contributes to integrating CBF and Bi-GRU to enhance the relevance of culinary recommendations. The system uses Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for item matching. Research adapting CBF and Bi-GRU methods specifically for culinary recommendations, especially in Bandung, remains limited. This study focuses on evaluating the performance of a culinary recommendation system. Data collected from Twitter (X) and PergiKuliner includes 2,645 reviews from 44 Twitter (X) accounts and on 200 culinary places. The culinary recommendation model, using CBF with TF-IDF and Cosine Similarity, achieved a Mean Absolute Error (MAE) of 0.254 and Root Mean Square Error (RMSE) of 0.425, indicating high accuracy in rating predictions compared to previous studies. From the experiments conducted, the third experiment using Bi-GRU, SMOTE, and the Nadam algorithm showed the best improvement with a learning rate of 0.014563484775012459, achieving an accuracy of 86.8%, precision of 86.3%, recall of 85.2%, and an F1-Score of 85.5%, with a 16.2% increase in accuracy from the baseline. Thus, this system effectively helps users with culinary recommendations in Bandung, providing good performance based on user preferences.

Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

Social media has become a vital part of most modern human personal life. Twitter is one of the social media that was formed from the development of communication technology. A lot of social media gives users the freedom to express themselves. This facility is misused by users, so hate speech is spread. Designing a system to detect hate speech intelligently is needed. This study uses the hybrid deep learning (HDL) and solo deep learning (SDL) approach with the convolutional neural networks (CNN) and bidirectional gated recurrent unit (Bi-GRU) algorithm. There are 4 models built, namely CNN, Bi-GRU, CNN+Bi-GRU, and Bi-GRU+CNN. Term frequency-inverse document frequency (TF-IDF) is used for feature extraction, which is to get linguistic features to be analyzed and studied. FastText is used to perform feature expansion to minimize mismatched vocabulary. Four scenarios are run. CNN with an accuracy of 87.63%, Bi-GRU produces an accuracy of 87.46%, CNN+Bi-GRU provides an accuracy of 87.47% and Bi-GRU+CNN provides an accuracy of 87.34%. The ability of this approach to understand the context is qualified. HDL outperforms SDL in terms of n-gram type, where HDL can understand sentences broken down by hybrid n-gram types, namely Unigram-Bigram-Trigram which is a complex n-gram hybrid.

Central air-conditioning control system have characteristics of susceptible to outside interference, nonlinear, time-varying, hysteresis and difficult to build precise mathematical model. Central air conditioning chilled water system with variable water flow was taken as subject. According to the principle of energy saving, on the basis of traditional PID controller, adaptive fuzzy PID controller was designed. Fuzzy control algorithm determines the choice of parameters of PID controller KP、KI、KD , with which, then, regulate the output of adaptive fuzzy PID controller; Finally the output value determination of the operation frequency of the chilled water pumps. Compared with conventional system temperature control, adaptive fuzzy PID control in central air conditioning system has good energy-saving effect.

Accurate temperature prediction is critical in diverse areas, such as agriculture, disaster management, and urban planning, where understanding climatic patterns is essential. This study explores the application of advanced deep-learning models for temperature forecasting, focusing on the model’s ability to establish complex relationships and temporal dependencies within climatic data. This study evaluates the performance of various deep-learning models for temperature prediction using environmental data. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU) models were developed and compared. The models were trained on meteorological data from Ranchi, India, spanning 2014-2024. Performance was assessed using Root Mean Square Error (RMSE), loss function analysis, and statistical significance testing. Results indicate that bidirectional architectures (BiLSTM and BiGRU) consistently outperformed unidirectional models. BiLSTM achieved the lowest RMSE and most balanced loss values across training, validation and test sets. The BiLSTM model performed well by 39.19.7% in RMSE and 15.36% in test loss. From the statistical analysis, BiLSTM is the best performer compared with BiGRU, with a negative t-statistic (-29.65) and a very low p-value (0.00000771), indicating a statistically significant difference.

The study of the dynamic load forecasting model about air-conditioning system based on the terminal user load

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

Research on energy-saving optimization method for central air conditioning system based on multi-strategy improved sparrow search algorithm

As the development of smart grids and electricity markets around the world, short-term load forecasting (STLF) plays an increasingly important role in safe and economical operations of power systems. Facing massive multivariate and heterogeneous data from smart grid environment, traditional STLF methods no longer meet the requirements of comprehensive prediction performance in big data era. Therefore, a novel STLF model named EGA-STLF is proposed in this paper. The core idea of the proposed model is mainly based on distributed representation, bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. EGA-STLF encodes non-numerical variables in input data into sparse vectors by distributed representation during preprocessing, which can more reasonably reflect the correlations between different variables across the temporal sequence. Moreover, the Bi-GRU layer in EGA-STLF processes the past and the future information simultaneously to fully extract temporal and nonlinear features from input data for the improvement of forecasting accuracy. In addition, the introduction of attention mechanism highlights the role of key features in load forecasting, which is beneficial to generate more accurate forecasting results for EGA-STLF. The validity and superiority of EGA-STLF is verified by taking the actual data from Victoria state and Queensland state in Australia for experimental research in which mean absolute percentage error (MAPE) and root mean square error (RMSE) are selected as evaluation metrics. The experimental results indicate that EGA-STLF outperforms the state-of-the-art models based on SVR and MLP in term of comprehensive forecasting accuracy. Hence, EGA-STLF is a promising method to create economic benefits in power industry.