桐乡市酒店管道蒸汽用量智慧管控数据

通过收集和分析酒店管道蒸汽用量的时间、总累积质量、总累积热量、瞬时流量、瞬时热量、瞬时温度、瞬时压力、密度、输入电流等相关数据，了解酒店行业对用热量的需求，以及对客户的用热量影响分析，能够更准确地预测其运营中的用热量，以便更好地进行能源管理和成本控制，利于酒店自我管控。酒店管道蒸汽用量智慧管控模型利用积累的蒸汽用量等数据，酒店可以进行详细的能耗分析，提前做好能源采购计划，降低采购成本。
准确的蒸汽用量数据有助于进行成本核算，从而合理调整服务价格，提高经济效益。该模型对其他酒店有借鉴意义，推动整个酒店行业向智慧化方向发展，提升酒店行业的整体服务水平和竞争力。选用卷积神经网络模型进行构建。步骤1：数据进行收集处理，整理为一个形状为(n_samples, 9)的numpy数组，管道蒸汽用量的时间、总累积质量、总累积热量、瞬时流量、瞬时热量、瞬时温度、瞬时压力、密度、输入电流分别为9个特征，再进行标准化处理，使得每个特征的均值为0，标准差为1。步骤2：利用python创建模型，添加一维卷积层、最大池化层，添加第二个卷积层、最大池化层，将卷积层的输出展平，添加全连接层，最后添加输出层，模型核心为使用一维卷积层来提取特征，然后通过最大池化层降低特征维度，将卷积层的输出展平后连接全连接层，最后输出一个预测值。步骤3：对模型进行编译，划分训练集、验证集和测试集，最后对输入数据进行形状调整，以适应卷积层的输入要求，再训练该模型。步骤4：测试和评估模型性能，绘制训练和验证损失曲线，观察训练过程，防止过拟合。步骤5：卷积神经网络模型输出预测蒸汽流量值和最高临界值为15.3t/h，当预测蒸汽流量值>15.3t/h，管道状态显示“管道异常”，当0≤预测蒸汽流量值≤15.3t/h，显示“管道正常”。

By collecting and analyzing relevant data including time stamps, total cumulative mass, total cumulative heat, instantaneous flow rate, instantaneous heat, instantaneous temperature, instantaneous pressure, density, and input current of hotel pipeline steam consumption, we can understand the heat demand of the hotel industry and analyze the impact of heat consumption on customers, thereby more accurately predicting the heat demand in hotel operations to achieve better energy management and cost control, and facilitate self-management of hotels. The intelligent management and control model for hotel pipeline steam consumption utilizes accumulated data such as steam consumption volumes, enabling hotels to conduct detailed energy consumption analysis, formulate energy procurement plans in advance, and reduce procurement costs. Accurate steam consumption data facilitates cost accounting, allowing for reasonable adjustment of service prices and improvement of economic benefits. This model can serve as a reference for other hotels, promoting the intellectualization of the entire hotel industry and enhancing the overall service level and competitiveness of the hotel sector. A Convolutional Neural Network (CNN) is selected for model construction. Step 1: Collect and process the data, organizing it into a numpy array with the shape of (n_samples, 9). The 9 features correspond to the time stamp, total cumulative mass, total cumulative heat, instantaneous flow rate, instantaneous heat, instantaneous temperature, instantaneous pressure, density, and input current of hotel pipeline steam consumption, respectively. Standardization is then performed to ensure that the mean and standard deviation of each feature are 0 and 1, respectively. Step 2: Develop the model using Python. Add a 1D convolutional layer and a max pooling layer, followed by a second 1D convolutional layer and another max pooling layer. Flatten the output of the convolutional layers, add a fully connected layer, and finally add an output layer. The core of the model lies in using 1D convolutional layers to extract features, reducing feature dimensionality via max pooling layers, flattening the output of the convolutional layers, connecting to the fully connected layer, and finally outputting a single predicted value. Step 3: Compile the model, split the dataset into training, validation, and test sets, adjust the shape of the input data to meet the input requirements of the convolutional layers, and then train the model. Step 4: Test and evaluate the model performance, plot the training and validation loss curves, monitor the training process, and prevent overfitting. Step 5: The CNN model outputs the predicted steam flow value, with a maximum critical threshold of 15.3 t/h. If the predicted steam flow value exceeds 15.3 t/h, the pipeline status is displayed as "Abnormal Pipeline"; if 0 ≤ predicted steam flow value ≤ 15.3 t/h, the status is displayed as "Normal Pipeline".

搜集汇总

数据集介绍

特点

该数据集包含桐乡市酒店管道蒸汽用量的详细信息，共576条数据，每日更新，用于能源管理和成本控制。数据包括客户名称、时间、总累积质量等12个字段，采用卷积神经网络模型进行预测和分析。