不同炉型在东北的需求分析数据

为洞察东北地区锅炉市场需求，需结合近三年各炉型需求数量、蒸发量差异梳理发展脉络。受严寒气候、煤炭资源禀赋与老工业基地转型影响，当地需求既凸显供暖刚需，又暗藏清洁化转型趋势，区域分化中藏着共性方向。辽宁作为工业重镇，沈阳机床、大连石化等企业老旧锅炉改造是核心需求，10-30t/h高效煤粉锅炉与循环流化床锅炉成替换主力，近三年采购量年均增长12%；冬季供暖季还拉动沈阳、鞍山采购20-40MW大型热水锅炉，兼顾工业用热与民生供暖。吉林需求带农业印记，年产能超3000万吨的玉米秸秆驱动生物质锅炉需求年均增20%以上，长春食品加工厂用秸秆锅炉保障生产供热，一汽配套产业链逐步普及燃气锅炉；松原农村则用小型生物质炉替代散煤采暖。黑龙江因供暖期达6个月，哈尔滨、大庆集中供暖对20-50MW高效燃煤热水锅炉需求稳定，但大庆石化等推进 “煤改气”，燃气锅炉采购量年增18%；伊春等林区用木材加工废弃物驱动生物质锅炉，形成特色市场。传统燃煤锅炉虽因供暖刚需仍有存量，但清洁炉型增长已勾勒转型路径，要求制造企业紧扣东北 “供暖保供 + 产业升级 + 资源利用” 特点，优化大吨位供热炉、生物质炉适配性精准布局。一：数据采集：企业CRM系统中采集近3年工业锅炉不同炉型在东北的需求数量和用热蒸发量数据。
二：算法规则：对采集得到的数据按照如下公式进行计算：
0、说明（当前锅炉型号总蒸发量=蒸发量*锅炉数量）
1、按照年份，省份进行数据透析，得出各个省份三年的总蒸发量
1、对三年的总蒸发量计算平均值x，
2、计算每个数据与平均值的绝对差，
3、计算平均绝对偏差MAD
4：计算相对平均偏差RMD
5、计算相对平均偏差RMD
例如（15,6.5,8）这组数据:（注意：这组数据仅作为举例算法，样例数据中平均值、MAD以及RMD是需要先省份求和再运算）
1.平均值x=9.83;
2.计算每个数据与平均值的绝对差:|15-9.83|=5.17；|6.5-9.83|=3.33；|8-9.83|=1.83；
3.平均绝对偏差MAD=（5.17+3.33+1.83）/3=3.44;
4.相对平均偏差RMD=3.44/9.83*100%=34.99%。
三、数据分析：根据RMD的数值可分析不同炉型在东北的需求量和用热蒸发量。根据计算得出的RMD值对炉型进行分级：亮点系列（RMD≤10%），重点系列（10%<RMD≤35%），普通系列（35%<RMD≤80%），低表现系列（RMD>80%）。

To gain insights into the boiler market demand in Northeast China, it is necessary to sort out the development trajectory by combining the demand quantity and evaporation capacity differences of various boiler types over the past three years. Affected by the frigid climate, endowment of coal resources, and the transformation of old industrial bases, local demand highlights both rigid heating demand and the hidden trend of clean transformation, with common directions underlying regional differentiation.

Liaoning, as an important industrial hub, regards the renovation of old boilers in enterprises such as Shenyang Machine Tool and Dalian Petrochemical as its core demand. High-efficiency pulverized coal boilers and circulating fluidized bed (CFB) boilers with a capacity of 10-30 t/h have become the main replacement options, with their annual procurement volume growing at an average rate of 12% over the past three years. The winter heating season also drives the procurement of 20-40 MW large-scale hot water boilers in Shenyang and Anshan, which serve both industrial heat supply and residential heating.

Jilin's demand bears distinct agricultural imprints. The demand for biomass boilers driven by corn straw with an annual production capacity exceeding 30 million tons grows at an average annual rate of over 20%. Straw boilers in Changchun's food processing factories are used to ensure production heat supply, and the supporting industrial chain of FAW (First Automobile Works) is gradually popularizing gas-fired boilers. In rural Songyuan, small biomass furnaces are used to replace raw coal heating.

Heilongjiang has a heating period of up to 6 months. The central heating projects in Harbin and Daqing have stable demand for 20-50 MW high-efficiency coal-fired hot water boilers. However, enterprises such as Daqing Petrochemical are promoting coal-to-gas transformation, with the procurement volume of gas-fired boilers growing at an annual rate of 18%. Forest regions such as Yichun use biomass boilers driven by wood processing waste, forming a characteristic local market.

Although traditional coal-fired boilers still maintain existing inventory due to rigid heating demand, the growth of clean boiler types has outlined the transformation path, requiring manufacturers to closely align with the characteristics of Northeast China's "heating supply guarantee + industrial upgrading + resource utilization", optimize the adaptability of large-tonnage heating furnaces and biomass boilers, and carry out precise layout.

I. Data Collection
Collect data on the demand quantity and heat evaporation capacity of different types of industrial boilers in Northeast China over the past three years from the enterprise's CRM (Customer Relationship Management) system.

II. Algorithm Rules
0. Note: Total evaporation capacity of a given boiler model = evaporation capacity × number of boilers
1. Perform data pivot analysis by year and province to obtain the total evaporation volume of each province across the three-year period.
2. Calculate the average value x of the three-year total evaporation volumes.
3. Calculate the absolute difference between each data point and the average value.
4. Calculate the Mean Absolute Deviation (MAD).
5. Calculate the Relative Mean Deviation (RMD).

Note: The sample data below is for illustration only; the actual calculation requires summing data by province first.
Take the data set (15, 6.5, 8) as an example:
1. Average value x = 9.83;
2. Calculate the absolute difference between each data point and the average value: |15 - 9.83| = 5.17; |6.5 - 9.83| = 3.33; |8 - 9.83| = 1.83;
3. Mean Absolute Deviation MAD = (5.17 + 3.33 + 1.83) / 3 = 3.44;
4. Relative Mean Deviation RMD = 3.44 / 9.83 × 100% = 34.99%.

III. Data Analysis
Analyze the demand and heat evaporation capacity of different boiler types in Northeast China based on the calculated RMD values. Classify boiler types according to the RMD values:
- Highlight Series (RMD ≤ 10%)
- Key Series (10% < RMD ≤ 35%)
- General Series (35% < RMD ≤ 80%)
- Low-Performance Series (RMD > 80%)

搜集汇总

背景与挑战

背景概述

该数据集聚焦东北地区锅炉市场需求分析，包含562条企业数据，每年更新，通过年份、省份、锅炉型号等字段，结合相对平均偏差算法对炉型需求进行分级。数据集揭示了东北地区因严寒气候和产业转型，清洁锅炉（如燃气和生物质锅炉）需求增长显著，同时传统燃煤锅炉仍有存量，适用于制造业企业优化产品布局。