以下是一个使用MapReduce来计算最大平均电力消耗的示例代码：

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MaxAveragePowerConsumption {

  public static class PowerConsumptionMapper extends Mapper{

    private Text location = new Text();
    private FloatWritable power = new FloatWritable();

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
      String line = value.toString();
      StringTokenizer tokenizer = new StringTokenizer(line, ",");
      
      String year = tokenizer.nextToken();
      String month = tokenizer.nextToken();
      String day = tokenizer.nextToken();
      location.set(tokenizer.nextToken());
      float powerConsumption = Float.parseFloat(tokenizer.nextToken());
      power.set(powerConsumption);
      
      context.write(location, power);
    }
  }

  public static class PowerConsumptionReducer extends Reducer {
    private FloatWritable result = new FloatWritable();

    public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
      float sum = 0;
      int count = 0;
      for (FloatWritable val : values) {
        sum += val.get();
        count++;
      }
      float average = sum / count;
      result.set(average);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "max average power consumption");
    job.setJarByClass(MaxAveragePowerConsumption.class);
    job.setMapperClass(PowerConsumptionMapper.class);
    job.setReducerClass(PowerConsumptionReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(FloatWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

请注意，上述代码假定CSV文件的格式如下：年份,月份,日期,地点,电力消耗。您需要根据您的CSV文件的实际格式进行适当的更改。

要运行这个MapReduce程序，请将上述代码保存为一个Java类文件，然后使用Hadoop命令行工具编译和运行它。假设您已经将Hadoop正确设置并运行，您可以使用以下命令来运行该程序：

hadoop jar  <完整类名> <输入文件路径> <输出文件路径>

请将替换为包含编译后的Java类文件的JAR文件的路径，<完整类名>替换为您的Java类的完整类名，<输入文件路径>替换为CSV文件的路径，<输出文件路径>替换为您希望将结果保存到的路径。

运行该程序后，您将在指定的输出文件路径中找到计算出的最大平均电力消耗结果。